{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/nmt/papers/8","list_of":"/task/nmt","task":"NMT","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":8,"pages_in_order":18,"rows_per_page":100,"rows":[701,800],"of":1773,"counts":{"archive_papers_tagged":1773,"with_a_code_link":523,"where_syntology_ran_a_sample":84,"not_listed_spam_title":0,"listed":1773,"listed_where_code_ran":84,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":67,"every_run_a_failure_of_syntologys_instrument":17,"listed_with_a_run_with_no_instrument_failure":67,"listed_every_run_a_failure_of_syntologys_instrument":17,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/nmt","prev":"/task/nmt/papers/7","next":"/task/nmt/papers/9","papers":[{"url":null,"slug":"linguistically-motivated-yoruba-english","title":"Linguistically-Motivated Yorùbá-English Machine Translation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nicts-submission-to-the-wat-2022-structured","title":"NICT’s Submission to the WAT 2022 Structured Document Translation Task","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nit-rourkela-machine-translation-mt-system","title":"NIT Rourkela Machine Translation(MT) System Submission to WAT 2022 for MultiIndicMT: An Indic Language Multilingual Shared Task","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"taking-actions-separately-a-bidirectionally","title":"Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tmu-nmt-system-with-automatic-post-editing-by","title":"TMU NMT System with Automatic Post-Editing by Multi-Source Levenshtein Transformer for the Restricted Translation Task of WAT 2022","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"translating-spanish-into-spanish-sign","title":"Translating Spanish into Spanish Sign Language: Combining Rules and Data-driven Approaches","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"blur-the-linguistic-boundary-interpreting","title":"Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation","date":"2022-09-30","arxiv_id":"2209.15164","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-improving-health-literacy-in-patient","title":"Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models","date":"2022-09-14","arxiv_id":"2209.06723","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-based-template-machine-translation-1","title":"Knowledge Based Template Machine Translation In Low-Resource Setting","date":"2022-09-08","arxiv_id":"2209.03554","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-data-filtering-methods-for","title":"A Comparison of Data Filtering Methods for Neural Machine Translation","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"all-you-need-is-source-a-study-on-source","title":"All You Need is Source! A Study on Source-based Quality Estimation for Neural Machine Translation","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"current-shortcomings-of-machine-translation","title":"Current Shortcomings of Machine Translation in Spanish and Bulgarian Vis-à-vis English","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"english-russian-data-augmentation-for-neural","title":"English-Russian Data Augmentation for Neural Machine Translation","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effectiveness-of-quasi-character-level-2","title":"On the Effectiveness of Quasi Character-Level Models for Machine Translation","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-translation-competence-on-error","title":"The impact of translation competence on error recognition of neural MT","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discourse-cohesion-evaluation-for-document","title":"Discourse Cohesion Evaluation for Document-Level Neural Machine Translation","date":"2022-08-19","arxiv_id":"2208.09118","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-effective-is-byte-pair-encoding-for-out","title":"How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?","date":"2022-08-10","arxiv_id":"2208.05225","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-azerbaijani-neural-machine","title":"Benchmarking Azerbaijani Neural Machine Translation","date":"2022-07-29","arxiv_id":"2207.14473","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-neural-machine-translation-with","title":"Multimodal Neural Machine Translation with Search Engine Based Image Retrieval","date":"2022-07-26","arxiv_id":"2208.00767","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-simultaneous-speech-translation","title":"End-to-End Simultaneous Speech Translation with Pretraining and Distillation: Huawei Noah’s System for AutoSimTranS 2022","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reduce-indonesian-vocabularies-with-an","title":"Reduce Indonesian Vocabularies with an Indonesian Sub-word Separator","date":"2022-07-01","arxiv_id":"2207.00552","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-disambiguation-biases-in-nmt-by","title":"Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-nmt-models-for-the-indian-1","title":"Unified NMT models for the Indian subcontinent, transcending script-barriers","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-causes-of-hallucinations-in-neural","title":"Probing Causes of Hallucinations in Neural Machine Translations","date":"2022-06-25","arxiv_id":"2206.12529","repositories_listed":0,"syntology":null},{"url":null,"slug":"freetransfer-x-safe-and-label-free-cross","title":"FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models","date":"2022-06-14","arxiv_id":"2206.06586","repositories_listed":0,"syntology":null},{"url":null,"slug":"dict-nmt-bilingual-dictionary-based-nmt-for-1","title":"Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages","date":"2022-06-09","arxiv_id":"2206.04439","repositories_listed":0,"syntology":null},{"url":null,"slug":"finetuning-a-kalaallisut-english-machine","title":"Finetuning a Kalaallisut-English machine translation system using web-crawled data","date":"2022-06-05","arxiv_id":"2206.02230","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-the-right-recipe-for-low-resource-1","title":"Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation","date":"2022-06-02","arxiv_id":"2206.01137","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-study-reveals-unexpected","title":"A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"achievements-of-the-principle-project","title":"Achievements of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-bilingual-phrase-dictionary","title":"Automatic Bilingual Phrase Dictionary Construction from GIZA++ Output","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bertology-for-machine-translation-what-bert","title":"BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-of-neural-machine-translation-for","title":"Challenges of Neural Machine Translation for Short Texts","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-and-combining-tagging-with","title":"Comparing and combining tagging with different decoding algorithms for back-translation in NMT: learnings from a low resource scenario","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-multilingual-nmt-models-and","title":"Comparing Multilingual NMT Models and Pivoting","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"creamt-creativity-and-narrative-engagement-of","title":"CREAMT: Creativity and narrative engagement of literary texts translated by translators and NMT","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-adaptation-of-neural-machine","title":"Dynamic Adaptation of Neural Machine-Translation Systems Through Translation Exemplars","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-pre-training-objectives-for-low","title":"Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-paced-improvements-to-named-entity","title":"Fast-Paced Improvements to Named Entity Handling for Neural Machine Translation","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"indeep-x-nmt-empowering-human-translators-via","title":"InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-em-ft-for-manipuri-english-neural","title":"Introducing EM-FT for Manipuri-English Neural Machine Translation","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-the-curlicat-corpora-seven","title":"Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"latest-development-in-the-fotran-project","title":"Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"low-resource-neural-machine-translation","title":"Low-resource Neural Machine Translation: Benchmarking State-of-the-art Transformer for Wolof<->French","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-of-16th-century-letters","title":"Machine Translation of 16Th Century Letters from Latin to German","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-neural-machine-translation-with-4","title":"Multilingual Neural Machine Translation With the Right Amount of Sharing","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multitrainmt-erasmus-project-machine","title":"MultitraiNMT Erasmus+ project: Machine Translation Training for multilingual citizens (multitrainmt.eu)","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"passing-parser-uncertainty-to-the-transformer","title":"Passing Parser Uncertainty to the Transformer. Labeled Dependency Distributions for Neural Machine Translation.","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-synthetic-cross-lingual-decoder","title":"Pre-training Synthetic Cross-lingual Decoder for Multilingual Samples Adaptation in E-Commerce Neural Machine Translation","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"priming-ancient-korean-neural-machine","title":"Priming Ancient Korean Neural Machine Translation","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-multilingual-microblog-translation-corpus","title":"The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-semantic-role-labeling-to-improve","title":"Using Semantic Role Labeling to Improve Neural Machine Translation","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"salted-a-framework-for-salient-long-tail","title":"SALTED: A Framework for SAlient Long-Tail Translation Error Detection","date":"2022-05-20","arxiv_id":"2205.09988","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-to-address-out-of","title":"Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation","date":"2022-05-18","arxiv_id":"2205.08722","repositories_listed":0,"syntology":null},{"url":null,"slug":"controlling-formality-in-low-resource-nmt","title":"Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022","date":"2022-05-12","arxiv_id":"2205.05990","repositories_listed":0,"syntology":null},{"url":null,"slug":"admix-a-mixed-sample-data-augmentation-method","title":"AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation","date":"2022-05-10","arxiv_id":"2205.04686","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-mixed-domain-translation-models-via","title":"Training Mixed-Domain Translation Models via Federated Learning","date":"2022-05-03","arxiv_id":"2205.01557","repositories_listed":0,"syntology":null},{"url":null,"slug":"jam-or-cream-first-modeling-ambiguity-in","title":"Jam or Cream First? Modeling Ambiguity in Neural Machine Translation with SCONES","date":"2022-05-02","arxiv_id":"2205.00704","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-implicit-length-bias-of-label-smoothing","title":"The Implicit Length Bias of Label Smoothing on Beam Search Decoding","date":"2022-05-02","arxiv_id":"2205.00659","repositories_listed":0,"syntology":null},{"url":null,"slug":"locality-sensitive-hashing-for-long-context","title":"Locality-Sensitive Hashing for Long Context Neural Machine Translation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-for-livonian-catering-to","title":"Machine Translation for Livonian: Catering to 20 Speakers","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-and-mitigating-name-biases-in","title":"Measuring and Mitigating Name Biases in Neural Machine Translation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nvidia-nemo-offline-speech-translation","title":"NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-target-representation-in-continuous-output","title":"On Target Representation in Continuous-output Neural Machine Translation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-difference-regularization-against","title":"Prediction Difference Regularization against Perturbation for Neural Machine Translation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"producing-standard-german-subtitles-for-swiss","title":"Producing Standard German Subtitles for Swiss German TV Content","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"translation-techies-dravidianlangtech-acl2022","title":"Translation Techies @DravidianLangTech-ACL2022-Machine Translation in Dravidian Languages","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-works-and-doesnt-work-a-deep-decoder-for","title":"What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-adapter-architecture-for-unsupervised","title":"Flow-Adapter Architecture for Unsupervised Machine Translation","date":"2022-04-26","arxiv_id":"2204.12225","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-do-contrastive-word-alignments-improve-1","title":"When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?","date":"2022-04-26","arxiv_id":"2204.12165","repositories_listed":0,"syntology":null},{"url":null,"slug":"dalc-domain-adaptation-learning-curve","title":"DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation","date":"2022-04-20","arxiv_id":"2204.09259","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-position-encoding-for-transformers","title":"Dynamic Position Encoding for Transformers","date":"2022-04-18","arxiv_id":"2204.08142","repositories_listed":0,"syntology":null},{"url":null,"slug":"c-nmt-a-collaborative-inference-framework-for","title":"C-NMT: A Collaborative Inference Framework for Neural Machine Translation","date":"2022-04-08","arxiv_id":"2204.04043","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-nmt-understand-me-towards-perturbation","title":"Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation","date":"2022-03-29","arxiv_id":"2203.15319","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-selection-curriculum-for-neural-machine","title":"Data Selection Curriculum for Neural Machine Translation","date":"2022-03-25","arxiv_id":"2203.13867","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-and-improving-sequence-to-1","title":"Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation","date":"2022-03-16","arxiv_id":"2203.08442","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-synthetic-translations-improve-bitext-1","title":"Can Synthetic Translations Improve Bitext Quality?","date":"2022-03-15","arxiv_id":"2203.07643","repositories_listed":0,"syntology":null},{"url":null,"slug":"look-backward-and-forward-self-knowledge","title":"Look Backward and Forward: Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation","date":"2022-03-10","arxiv_id":"2203.05248","repositories_listed":0,"syntology":null},{"url":null,"slug":"rmbr-a-regularized-minimum-bayes-risk","title":"RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation","date":"2022-03-01","arxiv_id":"2203.00201","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-based-bidirectional-global-context","title":"Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation","date":"2022-02-28","arxiv_id":"2202.13663","repositories_listed":0,"syntology":null},{"url":null,"slug":"refining-the-state-of-the-art-in-machine","title":"Refining the state-of-the-art in Machine Translation, optimizing NMT for the JA <-> EN language pair by leveraging personal domain expertise","date":"2022-02-23","arxiv_id":"2202.11669","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-in-neural-machine","title":"Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet","date":"2022-02-21","arxiv_id":"2202.10287","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-english-to-sinhala-neural-machine","title":"Improving English to Sinhala Neural Machine Translation using Part-of-Speech Tag","date":"2022-02-17","arxiv_id":"2202.08882","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-scaling-laws-in-nmt-the-effect-of-noise-1","title":"Data Scaling Laws in NMT: The Effect of Noise and Architecture","date":"2022-02-04","arxiv_id":"2202.01994","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-how-to-translate-north-korean","title":"Learning How to Translate North Korean through South Korean","date":"2022-01-27","arxiv_id":"2201.11258","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-simcut-a-simple-strategy-for-boosting","title":"Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"document-level-neural-machine-translation-4","title":"Document-level Neural Machine Translation Using Dependency RST Structure","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-based-template-machine-translation","title":"Knowledge Based Template Machine Translation In Low-Resource Setting","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nearest-neighbor-knowledge-distillation-for","title":"Nearest Neighbor Knowledge Distillation for Neural Machine Translation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neighbors-are-not-strangers-improving-non","title":"Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-synthetic-data-for-back-translation","title":"On Synthetic Data for Back Translation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effectiveness-of-quasi-character-level-1","title":"On the Effectiveness of Quasi Character-Level Models for Machine Translation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"patching-leaks-in-the-charformer-for","title":"Patching Leaks in the Charformer for Generative Tasks","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-with-large-action","title":"Reinforcement Learning with Large Action Spaces for Neural Machine Translation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-role-does-bert-play-in-the-neural","title":"What Role Does BERT Play in the Neural Machine Translation Encoder?","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"when-do-contrastive-word-alignments-improve","title":"When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-effective-training-in-low-resource-1","title":"Cost-Effective Training in Low-Resource Neural Machine Translation","date":"2022-01-14","arxiv_id":"2201.05700","repositories_listed":0,"syntology":null},{"url":null,"slug":"phrase-level-adversarial-example-generation","title":"PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation","date":"2022-01-06","arxiv_id":"2201.02009","repositories_listed":0,"syntology":null},{"url":null,"slug":"smdt-selective-memory-augmented-neural","title":"SMDT: Selective Memory-Augmented Neural Document Translation","date":"2022-01-05","arxiv_id":"2201.01631","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-aware-contrastive-learning-for","title":"Frequency-Aware Contrastive Learning for Neural Machine Translation","date":"2021-12-29","arxiv_id":"2112.14484","repositories_listed":0,"syntology":null}],"record_sha256":"15e197870b8787c3e3af1514074b74fef6032a3757273c355373a6161f880a4a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}