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GitHub - fallcat/stupidNMT: Hard-Coded Gaussian Attention for Neural Machine Translation
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1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":260275953,"defaultBranch":"master","name":"stupidNMT","ownerLogin":"fallcat","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2020-04-30T17:34:04.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/8541460?v=4","public":true,"private":false,"isOrgOwned":false},"currentUser":null,"refInfo":{"name":"master","listCacheKey":"v0:1684791574.2721379","canEdit":false,"refType":"branch","currentOid":"f8331f5f28c3cbcda8cc1d4eacffc97bea2d05c6"},"tree":{"items":[{"name":"actions","path":"actions","contentType":"directory"},{"name":"data","path":"data","contentType":"directory"},{"name":"metrics","path":"metrics","contentType":"directory"},{"name":"models","path":"models","contentType":"directory"},{"name":"process_new_data","path":"process_new_data","contentType":"directory"},{"name":"resources","path":"resources","contentType":"directory"},{"name":"scripts","path":"scripts","contentType":"directory"},{"name":"utils","path":"utils","contentType":"directory"},{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"__init__.py","path":"__init__.py","contentType":"file"},{"name":"args.py","path":"args.py","contentType":"file"},{"name":"main.py","path":"main.py","contentType":"file"},{"name":"requirements.txt","path":"requirements.txt","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":14,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/fallcat/stupidNMT/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/fallcat/stupidNMT.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh 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Hard-Coded Gaussian Attention for Neural Machine Translation\u003c/h1\u003e\u003ca id=\"user-content-stupidnmt-hard-coded-gaussian-attention-for-neural-machine-translation\" class=\"anchor\" aria-label=\"Permalink: StupidNMT: Hard-Coded Gaussian Attention for Neural Machine Translation\" href=\"#stupidnmt-hard-coded-gaussian-attention-for-neural-machine-translation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis is the official repository which contains all the code necessary to\nreplicate the results from the ACL 2020 long paper \u003cem\u003e\u003ca href=\"https://www.aclweb.org/anthology/2020.acl-main.687/\" rel=\"nofollow\"\u003eHard-Coded Gaussian Attention for Neural Machine Translation\u003c/a\u003e\u003c/em\u003e. It can also be used to\ntrain a vanilla Transformer.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe full model architecture is displayed below:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/fallcat/stupidNMT/blob/master/resources/model.png\"\u003e\u003cimg src=\"/fallcat/stupidNMT/raw/master/resources/model.png\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOur approach uses hard-coded Gaussian distribution instead of learned attention to simplify the Transformer architecture in neural machine translation (NMT). We replace the multi-headed attention, computed by query and key, by a fixed Gaussian distribution that focuses on the current word or somewhere near it. The figure above demonstrates how our attention differs from the vanilla Tranformer.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis code base is adapted from \u003ca href=\"https://github.com/dojoteef/synst\"\u003esynst\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe code requires Python 3.7+. The python dependencies can be installed with the\ncommand (using a virtual environment is highly recommended):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install -r requirements.txt\"\u003e\u003cpre\u003epip install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you want to use the scripts that wrap \u003ccode\u003emulti-bleu.perl\u003c/code\u003e and\n\u003ccode\u003esacrebleu\u003c/code\u003e, then you'll need to have\n\u003ca href=\"https://github.com/moses-smt/mosesdecoder\"\u003eMoses-SMT\u003c/a\u003e available as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBasic Usage\u003c/h2\u003e\u003ca id=\"user-content-basic-usage\" class=\"anchor\" aria-label=\"Permalink: Basic Usage\" href=\"#basic-usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe code has one main entry point \u003ccode\u003emain.py\u003c/code\u003e with a couple of support scripts for\nthe analysis conducted in the paper. Please use \u003ccode\u003epython main.py -h\u003c/code\u003e for\nadditional options not listed below. You can also use \u003ccode\u003epython main.py \u0026lt;action\u0026gt; -h\u003c/code\u003e for options specific to the available actions: \u003ccode\u003e{train, evaluate, translate, pass}\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePreprocessing\u003c/h3\u003e\u003ca id=\"user-content-preprocessing\" class=\"anchor\" aria-label=\"Permalink: Preprocessing\" href=\"#preprocessing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePreprocessing for wmt_en_de, wmt_en_fr and iwslt_en_de are the same as in synst:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python main.py --dataset wmt_en_de -d data/raw/wmt -p data/preprocessed/wmt -v pass\"\u003e\u003cpre\u003epython main.py --dataset wmt_en_de -d data/raw/wmt -p data/preprocessed/wmt -v pass\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe also added IWSLT En-Ja and WMT En-Ro datasets and you can process them by the following commands.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe need subword-nmt to process subwords for both En-Ja and En-Ro. WMT16-scripts is also needed for standard processing. Mosesdecoder is needed for tokenization. We download them with the following commands to directory of our choices and set the environment variables to those directories:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/rsennrich/subword-nmt.git\ngit clone https://github.com/rsennrich/wmt16-scripts.git\ngit clone https://github.com/moses-smt/mosesdecoder.git\nexport SUBWORD=subword-nmt\nexport WMT16_SCRIPTS=wmt16-scripts\nexport MOSES=mosesdecoder\"\u003e\u003cpre\u003egit clone https://github.com/rsennrich/subword-nmt.git\ngit clone https://github.com/rsennrich/wmt16-scripts.git\ngit clone https://github.com/moses-smt/mosesdecoder.git\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SUBWORD=subword-nmt\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e WMT16_SCRIPTS=wmt16-scripts\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MOSES=mosesdecoder\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen, we can download data for each dataset and process them by the following commands.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eEn-Ja:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"mkdir data/raw/enja\nmkdir data/processed/enja\nRAW_PATH=data/raw/enja PROCESS_PATH=data/processed/enja bash process_new_data/process_enja.sh\"\u003e\u003cpre\u003emkdir data/raw/enja\nmkdir data/processed/enja\nRAW_PATH=data/raw/enja PROCESS_PATH=data/processed/enja bash process_new_data/process_enja.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEn-Ro:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"mkdir data/raw/enro\nmkdir data/processed/enro\nRAW_PATH=data/raw/enro PROCESS_PATH=data/processed/enro bash process_new_data/process_enro.sh\"\u003e\u003cpre\u003emkdir data/raw/enro\nmkdir data/processed/enro\nRAW_PATH=data/raw/enro PROCESS_PATH=data/processed/enro bash process_new_data/process_enro.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease note that our preprocessing of En-Ja is not the standard way for IWSLT En-Ja dataset, so the results might be different from other works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining\u003c/h3\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAssuming you have access to 8 1080Ti GPUs you can recreate the results for hard-coded self-attention model on the WMT'14 En-De dataset with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python main.py -b 3175 --dataset wmt_en_de \\\n --model new_transformer \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n -d data/raw/wmt -p data/preprocessed/wmt -v train \\\n --checkpoint-interval 1200 --accumulate 2 \\\n --checkpoint-directory experiments/wmt_en_de_01\"\u003e\u003cpre\u003epython main.py -b 3175 --dataset wmt_en_de \\\n --model new_transformer \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n -d data/raw/wmt -p data/preprocessed/wmt -v train \\\n --checkpoint-interval 1200 --accumulate 2 \\\n --checkpoint-directory experiments/wmt_en_de_01\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe above commandline will train 8 GPUs with approximately 3175 source/target\ntokens combined per GPU, and accumulate the gradients over two batches before\nupdating model parameters (leading to ~50.8k tokens per model update).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can also recreate results on IWSLT'16 En-De dataset for hard-coded all attention model on 1 1080Ti GPU with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python main.py -b 6000 --dataset iwslt_en_de \\\n --model new_transformer \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n --enc-dec-attn-type normal --enc-dec-attn-offset -1 1 \\\n --embedding-size 288 --hidden-dim 507 --num-heads 4 --num-layers 5 \\\n -d data/raw/wmt -p data/preprocessed/wmt -v train \\\n --checkpoint-interval 600 --accumulate 1 \\\n --checkpoint-directory experiments/iwslt_en_de_01 \\\n --label-smoothing 0.0 --learning-rate-scheduler linear --learning-rate 3e-4\"\u003e\u003cpre\u003epython main.py -b 6000 --dataset iwslt_en_de \\\n --model new_transformer \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n --enc-dec-attn-type normal --enc-dec-attn-offset -1 1 \\\n --embedding-size 288 --hidden-dim 507 --num-heads 4 --num-layers 5 \\\n -d data/raw/wmt -p data/preprocessed/wmt -v train \\\n --checkpoint-interval 600 --accumulate 1 \\\n --checkpoint-directory experiments/iwslt_en_de_01 \\\n --label-smoothing 0.0 --learning-rate-scheduler linear --learning-rate 3e-4\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo recreate results for single head cross-attention, you can run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python main.py -b 6000 --dataset iwslt_en_de \\\n --model new_transformer \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n --enc-dec-attn-type learned \\\n --enc-dec-attn-layer 0 0 0 0 1 --enc-dec-attn-num-heads 0 0 0 0 1 \\\n --embedding-size 288 --hidden-dim 507 --num-heads 4 --num-layers 5 \\\n -d data/raw/wmt -p data/preprocessed/wmt -v train \\\n --checkpoint-interval 600 --accumulate 1 \\\n --checkpoint-directory experiments/iwslt_en_de_01 \\\n --label-smoothing 0.0 --learning-rate-scheduler linear --learning-rate 3e-4\"\u003e\u003cpre\u003epython main.py -b 6000 --dataset iwslt_en_de \\\n --model new_transformer \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n --enc-dec-attn-type learned \\\n --enc-dec-attn-layer 0 0 0 0 1 --enc-dec-attn-num-heads 0 0 0 0 1 \\\n --embedding-size 288 --hidden-dim 507 --num-heads 4 --num-layers 5 \\\n -d data/raw/wmt -p data/preprocessed/wmt -v train \\\n --checkpoint-interval 600 --accumulate 1 \\\n --checkpoint-directory experiments/iwslt_en_de_01 \\\n --label-smoothing 0.0 --learning-rate-scheduler linear --learning-rate 3e-4\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe default model is the Transformer model. If you want to train a vanilla Tranformer model on the WMT'14 De-En dataset, you can run this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python main.py -b 3175 --dataset wmt_de_en \\\n -d data/raw/wmt -p data/preprocessed/wmt -v train \\\n --checkpoint-interval 1200 --accumulate 2\"\u003e\u003cpre\u003epython main.py -b 3175 --dataset wmt_de_en \\\n -d data/raw/wmt -p data/preprocessed/wmt -v train \\\n --checkpoint-interval 1200 --accumulate 2\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvalulating Perplexity\u003c/h3\u003e\u003ca id=\"user-content-evalulating-perplexity\" class=\"anchor\" aria-label=\"Permalink: Evalulating Perplexity\" href=\"#evalulating-perplexity\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can run a separate process to evaluate each new checkpoint generated during\ntraining (you may either want to do it on a GPU not used for training or disable\ncuda as done below):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python main.py -b 5000 --dataset wmt_en_de \\\n --model new_transformer -d data/raw/wmt -p data/preprocessed/wmt \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n --split valid --disable-cuda -v evaluate \\\n --watch-directory /tmp/stupidnmt/checkpoints\"\u003e\u003cpre\u003epython main.py -b 5000 --dataset wmt_en_de \\\n --model new_transformer -d data/raw/wmt -p data/preprocessed/wmt \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n --split valid --disable-cuda -v evaluate \\\n --watch-directory /tmp/stupidnmt/checkpoints\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTranslating\u003c/h3\u003e\u003ca id=\"user-content-translating\" class=\"anchor\" aria-label=\"Permalink: Translating\" href=\"#translating\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAfter training a model, you can generate translations with the following\ncommand (currently only translation on a single GPU is supported):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"CUDA_VISIBLE_DEVICES=0 python main.py --dataset wmt_en_de \\\n --model new_transformer \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n -d data/raw/wmt -p data/preprocessed/wmt \\\n --batch-size 1 --batch-method example --split test \\\n --restore /tmp/stupidnmt/checkpoints/checkpoint.pt \\\n --average-checkpoints 5 translate \\\n --max-decode-length 50 --length-basis input_lens --order-output\"\u003e\u003cpre\u003eCUDA_VISIBLE_DEVICES=0 python main.py --dataset wmt_en_de \\\n --model new_transformer \\\n --enc-attn-type normal --enc-attn-offset -1 1 \\\n --dec-attn-type normal --dec-attn-offset -1 0 \\\n -d data/raw/wmt -p data/preprocessed/wmt \\\n --batch-size 1 --batch-method example --split \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e \\\n --restore /tmp/stupidnmt/checkpoints/checkpoint.pt \\\n --average-checkpoints 5 translate \\\n --max-decode-length 50 --length-basis input_lens --order-output\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhich by default, will output translations to \u003ccode\u003e/tmp/stupidnmt/output\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExperiment tracking\u003c/h3\u003e\u003ca id=\"user-content-experiment-tracking\" class=\"anchor\" aria-label=\"Permalink: Experiment tracking\" href=\"#experiment-tracking\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you have a \u003ca href=\"https://comet.ml\" rel=\"nofollow\"\u003ecomet.ml\u003c/a\u003e account, on you can track\nexperiments, by prefixing the script call with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"env $(cat ~/.comet.ml | xargs) python main.py --track ...\"\u003e\u003cpre\u003eenv \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ecat \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.comet.ml \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e xargs\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e python main.py --track ...\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhere \u003ccode\u003e~/.comet.ml\u003c/code\u003e is the file which contains your API key for logging\nexperiments on the service. By default, this will track experiments in a\nworkspace named \u003ccode\u003eumass-nlp\u003c/code\u003e with project name \u003ccode\u003eprobe-transformer\u003c/code\u003e. See \u003ccode\u003eargs.py\u003c/code\u003e in order to\nconfigure the experiment tracking to suit your needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCite\u003c/h2\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-label=\"Permalink: Cite\" href=\"#cite\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{you-etal-2020-hard,\n title = \u0026quot;Hard-Coded {G}aussian Attention for Neural Machine Translation\u0026quot;,\n author = \u0026quot;You, Weiqiu and\n Sun, Simeng and\n Iyyer, Mohit\u0026quot;,\n booktitle = \u0026quot;Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\u0026quot;,\n month = jul,\n year = \u0026quot;2020\u0026quot;,\n address = \u0026quot;Online\u0026quot;,\n publisher = \u0026quot;Association for Computational Linguistics\u0026quot;,\n url = \u0026quot;https://www.aclweb.org/anthology/2020.acl-main.687\u0026quot;,\n pages = \u0026quot;7689--7700\u0026quot;,\n abstract = \u0026quot;Recent work has questioned the importance of the Transformer{'}s multi-headed attention for achieving high translation quality. We push further in this direction by developing a {``}hard-coded{''} attention variant without any learned parameters. Surprisingly, replacing all learned self-attention heads in the encoder and decoder with fixed, input-agnostic Gaussian distributions minimally impacts BLEU scores across four different language pairs. However, additionally, hard-coding cross attention (which connects the decoder to the encoder) significantly lowers BLEU, suggesting that it is more important than self-attention. Much of this BLEU drop can be recovered by adding just a single learned cross attention head to an otherwise hard-coded Transformer. Taken as a whole, our results offer insight into which components of the Transformer are actually important, which we hope will guide future work into the development of simpler and more efficient attention-based models.\u0026quot;,\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{you-etal-2020-hard,\n title = \"Hard-Coded {G}aussian Attention for Neural Machine Translation\",\n author = \"You, Weiqiu and\n Sun, Simeng and\n Iyyer, Mohit\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.687\",\n pages = \"7689--7700\",\n abstract = \"Recent work has questioned the importance of the Transformer{'}s multi-headed attention for achieving high translation quality. We push further in this direction by developing a {``}hard-coded{''} attention variant without any learned parameters. Surprisingly, replacing all learned self-attention heads in the encoder and decoder with fixed, input-agnostic Gaussian distributions minimally impacts BLEU scores across four different language pairs. However, additionally, hard-coding cross attention (which connects the decoder to the encoder) significantly lowers BLEU, suggesting that it is more important than self-attention. Much of this BLEU drop can be recovered by adding just a single learned cross attention head to an otherwise hard-coded Transformer. Taken as a whole, our results offer insight into which components of the Transformer are actually important, which we hope will guide future work into the development of simpler and more efficient attention-based models.\",\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"StupidNMT: Hard-Coded Gaussian Attention for Neural Machine Translation","anchor":"stupidnmt-hard-coded-gaussian-attention-for-neural-machine-translation","htmlText":"StupidNMT: Hard-Coded Gaussian Attention for Neural Machine Translation"},{"level":2,"text":"Requirements","anchor":"requirements","htmlText":"Requirements"},{"level":2,"text":"Basic Usage","anchor":"basic-usage","htmlText":"Basic Usage"},{"level":3,"text":"Preprocessing","anchor":"preprocessing","htmlText":"Preprocessing"},{"level":3,"text":"Training","anchor":"training","htmlText":"Training"},{"level":3,"text":"Evalulating Perplexity","anchor":"evalulating-perplexity","htmlText":"Evalulating Perplexity"},{"level":3,"text":"Translating","anchor":"translating","htmlText":"Translating"},{"level":3,"text":"Experiment tracking","anchor":"experiment-tracking","htmlText":"Experiment tracking"},{"level":2,"text":"Cite","anchor":"cite","htmlText":"Cite"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ffallcat%2FstupidNMT"}},{"displayName":"LICENSE","repoName":"stupidNMT","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"BSD-3-Clause","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ffallcat%2FstupidNMT"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> 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data-loading="false" data-size="medium" aria-describedby=":Rr9ab:-loading-announcement" id=":Rr9ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">StupidNMT: Hard-Coded Gaussian Attention for Neural Machine Translation</h1><a id="user-content-stupidnmt-hard-coded-gaussian-attention-for-neural-machine-translation" class="anchor" aria-label="Permalink: StupidNMT: Hard-Coded Gaussian Attention for Neural Machine Translation" href="#stupidnmt-hard-coded-gaussian-attention-for-neural-machine-translation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This is the official repository which contains all the code necessary to replicate the results from the ACL 2020 long paper <em><a href="https://www.aclweb.org/anthology/2020.acl-main.687/" rel="nofollow">Hard-Coded Gaussian Attention for Neural Machine Translation</a></em>. It can also be used to train a vanilla Transformer.</p> <p dir="auto">The full model architecture is displayed below:</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/fallcat/stupidNMT/blob/master/resources/model.png"><img src="/fallcat/stupidNMT/raw/master/resources/model.png" alt="image" style="max-width: 100%;"></a></p> <p dir="auto">Our approach uses hard-coded Gaussian distribution instead of learned attention to simplify the Transformer architecture in neural machine translation (NMT). We replace the multi-headed attention, computed by query and key, by a fixed Gaussian distribution that focuses on the current word or somewhere near it. The figure above demonstrates how our attention differs from the vanilla Tranformer.</p> <p dir="auto">This code base is adapted from <a href="https://github.com/dojoteef/synst">synst</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Requirements</h2><a id="user-content-requirements" class="anchor" aria-label="Permalink: Requirements" href="#requirements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The code requires Python 3.7+. The python dependencies can be installed with the command (using a virtual environment is highly recommended):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install -r requirements.txt"><pre>pip install -r requirements.txt</pre></div> <p dir="auto">If you want to use the scripts that wrap <code>multi-bleu.perl</code> and <code>sacrebleu</code>, then you'll need to have <a href="https://github.com/moses-smt/mosesdecoder">Moses-SMT</a> available as well.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Basic Usage</h2><a id="user-content-basic-usage" class="anchor" aria-label="Permalink: Basic Usage" href="#basic-usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The code has one main entry point <code>main.py</code> with a couple of support scripts for the analysis conducted in the paper. Please use <code>python main.py -h</code> for additional options not listed below. You can also use <code>python main.py <action> -h</code> for options specific to the available actions: <code>{train, evaluate, translate, pass}</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Preprocessing</h3><a id="user-content-preprocessing" class="anchor" aria-label="Permalink: Preprocessing" href="#preprocessing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Preprocessing for wmt_en_de, wmt_en_fr and iwslt_en_de are the same as in synst:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python main.py --dataset wmt_en_de -d data/raw/wmt -p data/preprocessed/wmt -v pass"><pre>python main.py --dataset wmt_en_de -d data/raw/wmt -p data/preprocessed/wmt -v pass</pre></div> <p dir="auto">We also added IWSLT En-Ja and WMT En-Ro datasets and you can process them by the following commands.</p> <p dir="auto">We need subword-nmt to process subwords for both En-Ja and En-Ro. WMT16-scripts is also needed for standard processing. Mosesdecoder is needed for tokenization. We download them with the following commands to directory of our choices and set the environment variables to those directories:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/rsennrich/subword-nmt.git git clone https://github.com/rsennrich/wmt16-scripts.git git clone https://github.com/moses-smt/mosesdecoder.git export SUBWORD=subword-nmt export WMT16_SCRIPTS=wmt16-scripts export MOSES=mosesdecoder"><pre>git clone https://github.com/rsennrich/subword-nmt.git git clone https://github.com/rsennrich/wmt16-scripts.git git clone https://github.com/moses-smt/mosesdecoder.git <span class="pl-k">export</span> SUBWORD=subword-nmt <span class="pl-k">export</span> WMT16_SCRIPTS=wmt16-scripts <span class="pl-k">export</span> MOSES=mosesdecoder</pre></div> <p dir="auto">Then, we can download data for each dataset and process them by the following commands.</p> <p dir="auto">En-Ja:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="mkdir data/raw/enja mkdir data/processed/enja RAW_PATH=data/raw/enja PROCESS_PATH=data/processed/enja bash process_new_data/process_enja.sh"><pre>mkdir data/raw/enja mkdir data/processed/enja RAW_PATH=data/raw/enja PROCESS_PATH=data/processed/enja bash process_new_data/process_enja.sh</pre></div> <p dir="auto">En-Ro:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="mkdir data/raw/enro mkdir data/processed/enro RAW_PATH=data/raw/enro PROCESS_PATH=data/processed/enro bash process_new_data/process_enro.sh"><pre>mkdir data/raw/enro mkdir data/processed/enro RAW_PATH=data/raw/enro PROCESS_PATH=data/processed/enro bash process_new_data/process_enro.sh</pre></div> <p dir="auto">Please note that our preprocessing of En-Ja is not the standard way for IWSLT En-Ja dataset, so the results might be different from other works.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Training</h3><a id="user-content-training" class="anchor" aria-label="Permalink: Training" href="#training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Assuming you have access to 8 1080Ti GPUs you can recreate the results for hard-coded self-attention model on the WMT'14 En-De dataset with:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python main.py -b 3175 --dataset wmt_en_de \ --model new_transformer \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ -d data/raw/wmt -p data/preprocessed/wmt -v train \ --checkpoint-interval 1200 --accumulate 2 \ --checkpoint-directory experiments/wmt_en_de_01"><pre>python main.py -b 3175 --dataset wmt_en_de \ --model new_transformer \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ -d data/raw/wmt -p data/preprocessed/wmt -v train \ --checkpoint-interval 1200 --accumulate 2 \ --checkpoint-directory experiments/wmt_en_de_01</pre></div> <p dir="auto">The above commandline will train 8 GPUs with approximately 3175 source/target tokens combined per GPU, and accumulate the gradients over two batches before updating model parameters (leading to ~50.8k tokens per model update).</p> <p dir="auto">You can also recreate results on IWSLT'16 En-De dataset for hard-coded all attention model on 1 1080Ti GPU with:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python main.py -b 6000 --dataset iwslt_en_de \ --model new_transformer \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ --enc-dec-attn-type normal --enc-dec-attn-offset -1 1 \ --embedding-size 288 --hidden-dim 507 --num-heads 4 --num-layers 5 \ -d data/raw/wmt -p data/preprocessed/wmt -v train \ --checkpoint-interval 600 --accumulate 1 \ --checkpoint-directory experiments/iwslt_en_de_01 \ --label-smoothing 0.0 --learning-rate-scheduler linear --learning-rate 3e-4"><pre>python main.py -b 6000 --dataset iwslt_en_de \ --model new_transformer \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ --enc-dec-attn-type normal --enc-dec-attn-offset -1 1 \ --embedding-size 288 --hidden-dim 507 --num-heads 4 --num-layers 5 \ -d data/raw/wmt -p data/preprocessed/wmt -v train \ --checkpoint-interval 600 --accumulate 1 \ --checkpoint-directory experiments/iwslt_en_de_01 \ --label-smoothing 0.0 --learning-rate-scheduler linear --learning-rate 3e-4</pre></div> <p dir="auto">To recreate results for single head cross-attention, you can run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python main.py -b 6000 --dataset iwslt_en_de \ --model new_transformer \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ --enc-dec-attn-type learned \ --enc-dec-attn-layer 0 0 0 0 1 --enc-dec-attn-num-heads 0 0 0 0 1 \ --embedding-size 288 --hidden-dim 507 --num-heads 4 --num-layers 5 \ -d data/raw/wmt -p data/preprocessed/wmt -v train \ --checkpoint-interval 600 --accumulate 1 \ --checkpoint-directory experiments/iwslt_en_de_01 \ --label-smoothing 0.0 --learning-rate-scheduler linear --learning-rate 3e-4"><pre>python main.py -b 6000 --dataset iwslt_en_de \ --model new_transformer \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ --enc-dec-attn-type learned \ --enc-dec-attn-layer 0 0 0 0 1 --enc-dec-attn-num-heads 0 0 0 0 1 \ --embedding-size 288 --hidden-dim 507 --num-heads 4 --num-layers 5 \ -d data/raw/wmt -p data/preprocessed/wmt -v train \ --checkpoint-interval 600 --accumulate 1 \ --checkpoint-directory experiments/iwslt_en_de_01 \ --label-smoothing 0.0 --learning-rate-scheduler linear --learning-rate 3e-4</pre></div> <p dir="auto">The default model is the Transformer model. If you want to train a vanilla Tranformer model on the WMT'14 De-En dataset, you can run this:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python main.py -b 3175 --dataset wmt_de_en \ -d data/raw/wmt -p data/preprocessed/wmt -v train \ --checkpoint-interval 1200 --accumulate 2"><pre>python main.py -b 3175 --dataset wmt_de_en \ -d data/raw/wmt -p data/preprocessed/wmt -v train \ --checkpoint-interval 1200 --accumulate 2</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Evalulating Perplexity</h3><a id="user-content-evalulating-perplexity" class="anchor" aria-label="Permalink: Evalulating Perplexity" href="#evalulating-perplexity"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can run a separate process to evaluate each new checkpoint generated during training (you may either want to do it on a GPU not used for training or disable cuda as done below):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python main.py -b 5000 --dataset wmt_en_de \ --model new_transformer -d data/raw/wmt -p data/preprocessed/wmt \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ --split valid --disable-cuda -v evaluate \ --watch-directory /tmp/stupidnmt/checkpoints"><pre>python main.py -b 5000 --dataset wmt_en_de \ --model new_transformer -d data/raw/wmt -p data/preprocessed/wmt \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ --split valid --disable-cuda -v evaluate \ --watch-directory /tmp/stupidnmt/checkpoints</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Translating</h3><a id="user-content-translating" class="anchor" aria-label="Permalink: Translating" href="#translating"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">After training a model, you can generate translations with the following command (currently only translation on a single GPU is supported):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="CUDA_VISIBLE_DEVICES=0 python main.py --dataset wmt_en_de \ --model new_transformer \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ -d data/raw/wmt -p data/preprocessed/wmt \ --batch-size 1 --batch-method example --split test \ --restore /tmp/stupidnmt/checkpoints/checkpoint.pt \ --average-checkpoints 5 translate \ --max-decode-length 50 --length-basis input_lens --order-output"><pre>CUDA_VISIBLE_DEVICES=0 python main.py --dataset wmt_en_de \ --model new_transformer \ --enc-attn-type normal --enc-attn-offset -1 1 \ --dec-attn-type normal --dec-attn-offset -1 0 \ -d data/raw/wmt -p data/preprocessed/wmt \ --batch-size 1 --batch-method example --split <span class="pl-c1">test</span> \ --restore /tmp/stupidnmt/checkpoints/checkpoint.pt \ --average-checkpoints 5 translate \ --max-decode-length 50 --length-basis input_lens --order-output</pre></div> <p dir="auto">Which by default, will output translations to <code>/tmp/stupidnmt/output</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Experiment tracking</h3><a id="user-content-experiment-tracking" class="anchor" aria-label="Permalink: Experiment tracking" href="#experiment-tracking"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you have a <a href="https://comet.ml" rel="nofollow">comet.ml</a> account, on you can track experiments, by prefixing the script call with:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="env $(cat ~/.comet.ml | xargs) python main.py --track ..."><pre>env <span class="pl-s"><span class="pl-pds">$(</span>cat <span class="pl-k">~</span>/.comet.ml <span class="pl-k">|</span> xargs<span class="pl-pds">)</span></span> python main.py --track ...</pre></div> <p dir="auto">Where <code>~/.comet.ml</code> is the file which contains your API key for logging experiments on the service. By default, this will track experiments in a workspace named <code>umass-nlp</code> with project name <code>probe-transformer</code>. See <code>args.py</code> in order to configure the experiment tracking to suit your needs.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Cite</h2><a id="user-content-cite" class="anchor" aria-label="Permalink: Cite" href="#cite"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{you-etal-2020-hard, title = "Hard-Coded {G}aussian Attention for Neural Machine Translation", author = "You, Weiqiu and Sun, Simeng and Iyyer, Mohit", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.687", pages = "7689--7700", abstract = "Recent work has questioned the importance of the Transformer{'}s multi-headed attention for achieving high translation quality. We push further in this direction by developing a {``}hard-coded{''} attention variant without any learned parameters. Surprisingly, replacing all learned self-attention heads in the encoder and decoder with fixed, input-agnostic Gaussian distributions minimally impacts BLEU scores across four different language pairs. However, additionally, hard-coding cross attention (which connects the decoder to the encoder) significantly lowers BLEU, suggesting that it is more important than self-attention. Much of this BLEU drop can be recovered by adding just a single learned cross attention head to an otherwise hard-coded Transformer. Taken as a whole, our results offer insight into which components of the Transformer are actually important, which we hope will guide future work into the development of simpler and more efficient attention-based models.", }"><pre class="notranslate"><code>@inproceedings{you-etal-2020-hard, title = "Hard-Coded {G}aussian Attention for Neural Machine Translation", author = "You, Weiqiu and Sun, Simeng and Iyyer, Mohit", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.687", pages = "7689--7700", abstract = "Recent work has questioned the importance of the Transformer{'}s multi-headed attention for achieving high translation quality. We push further in this direction by developing a {``}hard-coded{''} attention variant without any learned parameters. Surprisingly, replacing all learned self-attention heads in the encoder and decoder with fixed, input-agnostic Gaussian distributions minimally impacts BLEU scores across four different language pairs. However, additionally, hard-coding cross attention (which connects the decoder to the encoder) significantly lowers BLEU, suggesting that it is more important than self-attention. Much of this BLEU drop can be recovered by adding just a single learned cross attention head to an otherwise hard-coded Transformer. Taken as a whole, our results offer insight into which components of the Transformer are actually important, which we hope will guide future work into the development of simpler and more efficient attention-based models.", } </code></pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="aKoTdFmbeRjx64fz83ZeMa9yGsQ8XKjBNhsVPypag/xb1IhVJ+EkfyEy4jcgK1dwlXE0C+p/kcuedrpC6lLpSg==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Hard-Coded Gaussian Attention for Neural Machine Translation </p> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" 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