{"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/7","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":7,"pages_in_order":18,"rows_per_page":100,"rows":[601,700],"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/6","next":"/task/nmt/papers/8","papers":[{"url":null,"slug":"asynchronous-and-segmented-bidirectional","title":"Asynchronous and Segmented Bidirectional Encoding for NMT","date":"2024-02-19","arxiv_id":"2402.14849","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-ad-referendum-how-good","title":"Large Language Models \"Ad Referendum\": How Good Are They at Machine Translation in the Legal Domain?","date":"2024-02-12","arxiv_id":"2402.07681","repositories_listed":0,"syntology":null},{"url":null,"slug":"promoting-target-data-in-context-aware-neural","title":"Promoting Target Data in Context-aware Neural Machine Translation","date":"2024-02-09","arxiv_id":"2402.06342","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-machine-translation-for-malayalam","title":"Neural Machine Translation for Malayalam Paraphrase Generation","date":"2024-01-31","arxiv_id":"2401.17827","repositories_listed":0,"syntology":null},{"url":null,"slug":"only-send-what-you-need-learning-to","title":"Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation","date":"2024-01-15","arxiv_id":"2401.07456","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approach-for-mistranslation-removal-from","title":"An approach for mistranslation removal from popular dataset for Indic MT Task","date":"2024-01-12","arxiv_id":"2401.06398","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-hindi-to-english-speech-conversion","title":"End to end Hindi to English speech conversion using Bark, mBART and a finetuned XLSR Wav2Vec2","date":"2024-01-11","arxiv_id":"2401.06183","repositories_listed":0,"syntology":null},{"url":"/paper/pomp-probability-driven-meta-graph-prompter","slug":"pomp-probability-driven-meta-graph-prompter","title":"POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation","date":"2024-01-11","arxiv_id":"2401.05596","repositories_listed":0,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pomp-probability-driven-meta-graph-prompter#ran","syntology_url":"https://syntology.ai/paper/2401.05596","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.05596"}},"official":null}},{"url":null,"slug":"can-chatgpt-rival-neural-machine-translation","title":"Convergences and Divergences between Automatic Assessment and Human Evaluation: Insights from Comparing ChatGPT-Generated Translation and Neural Machine Translation","date":"2024-01-10","arxiv_id":"2401.05176","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-unsupervised-neural","title":"An Empirical study of Unsupervised Neural Machine Translation: analyzing NMT output, model's behavior and sentences' contribution","date":"2023-12-19","arxiv_id":"2312.12588","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-human-translation-difficulty-with","title":"Predicting Human Translation Difficulty with Neural Machine Translation","date":"2023-12-19","arxiv_id":"2312.11852","repositories_listed":0,"syntology":null},{"url":null,"slug":"distinguishing-translations-by-human-nmt-and","title":"Distinguishing Translations by Human, NMT, and ChatGPT: A Linguistic and Statistical Approach","date":"2023-12-17","arxiv_id":"2312.10750","repositories_listed":0,"syntology":null},{"url":null,"slug":"unraveling-key-factors-of-knowledge","title":"Unraveling Key Factors of Knowledge Distillation","date":"2023-12-14","arxiv_id":"2312.08585","repositories_listed":0,"syntology":null},{"url":null,"slug":"order-matters-in-the-presence-of-dataset-1","title":"Order Matters in the Presence of Dataset Imbalance for Multilingual Learning","date":"2023-12-11","arxiv_id":"2312.06134","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-neural-machine-translation-by-multi","title":"Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting","date":"2023-12-08","arxiv_id":"2312.04807","repositories_listed":0,"syntology":null},{"url":null,"slug":"simul-llm-a-framework-for-exploring-high","title":"Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models","date":"2023-12-07","arxiv_id":"2312.04691","repositories_listed":0,"syntology":null},{"url":null,"slug":"relevance-guided-neural-machine-translation","title":"Relevance-guided Neural Machine Translation","date":"2023-11-30","arxiv_id":"2312.00214","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-gender-bias-in-machine-translation","title":"Reducing Gender Bias in Machine Translation through Counterfactual Data Generation","date":"2023-11-27","arxiv_id":"2311.16362","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-fly-fusion-of-large-language-models","title":"On-the-Fly Fusion of Large Language Models and Machine Translation","date":"2023-11-14","arxiv_id":"2311.08306","repositories_listed":0,"syntology":null},{"url":null,"slug":"memorisation-cartography-mapping-out-the","title":"Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation","date":"2023-11-09","arxiv_id":"2311.05379","repositories_listed":0,"syntology":null},{"url":null,"slug":"there-s-no-data-like-better-data-using-qe","title":"There's no Data Like Better Data: Using QE Metrics for MT Data Filtering","date":"2023-11-09","arxiv_id":"2311.05350","repositories_listed":0,"syntology":null},{"url":"/paper/integrating-pre-trained-language-model-into","slug":"integrating-pre-trained-language-model-into","title":"Integrating Pre-trained Language Model into Neural Machine Translation","date":"2023-10-30","arxiv_id":"2310.19680","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-refinement-of-translations-large","title":"Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing","date":"2023-10-23","arxiv_id":"2310.14855","repositories_listed":0,"syntology":null},{"url":null,"slug":"code-switching-with-word-senses-for","title":"Code-Switching with Word Senses for Pretraining in Neural Machine Translation","date":"2023-10-21","arxiv_id":"2310.14050","repositories_listed":0,"syntology":null},{"url":null,"slug":"ask-language-model-to-clean-your-noisy","title":"Ask Language Model to Clean Your Noisy Translation Data","date":"2023-10-20","arxiv_id":"2310.13469","repositories_listed":0,"syntology":null},{"url":null,"slug":"direct-neural-machine-translation-with-task","title":"Direct Neural Machine Translation with Task-level Mixture of Experts models","date":"2023-10-18","arxiv_id":"2310.12236","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-control-at-your-fingertips-quality","title":"Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model","date":"2023-10-10","arxiv_id":"2310.06707","repositories_listed":0,"syntology":null},{"url":null,"slug":"hindi-to-english-transformer-based-neural","title":"Hindi to English: Transformer-Based Neural Machine Translation","date":"2023-09-23","arxiv_id":"2309.13222","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-arabic-machine","title":"Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts","date":"2023-09-22","arxiv_id":"2309.12863","repositories_listed":0,"syntology":null},{"url":null,"slug":"osn-mdad-machine-translation-dataset-for","title":"OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media","date":"2023-09-21","arxiv_id":"2309.12137","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-effective-disambiguation-for-machine","title":"Towards Effective Disambiguation for Machine Translation with Large Language Models","date":"2023-09-20","arxiv_id":"2309.11668","repositories_listed":0,"syntology":null},{"url":null,"slug":"mbr-and-qe-finetuning-training-time","title":"MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods","date":"2023-09-19","arxiv_id":"2309.10966","repositories_listed":0,"syntology":null},{"url":null,"slug":"epi-curriculum-episodic-curriculum-learning","title":"Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation","date":"2023-09-06","arxiv_id":"2309.02640","repositories_listed":0,"syntology":null},{"url":null,"slug":"gender-specific-machine-translation-with","title":"Gender-specific Machine Translation with Large Language Models","date":"2023-09-06","arxiv_id":"2309.03175","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-context-all-you-need-scaling-neural-sign","title":"Is context all you need? Scaling Neural Sign Language Translation to Large Domains of Discourse","date":"2023-08-18","arxiv_id":"2308.09622","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-training-of-nmt-model-with-data-sorting","title":"Fast Training of NMT Model with Data Sorting","date":"2023-08-16","arxiv_id":"2308.08153","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-and-optimizing-the-effectiveness","title":"Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark","date":"2023-08-09","arxiv_id":"2308.04693","repositories_listed":0,"syntology":null},{"url":null,"slug":"character-level-nmt-and-language-similarity","title":"Character-level NMT and language similarity","date":"2023-08-08","arxiv_id":"2308.04398","repositories_listed":0,"syntology":null},{"url":null,"slug":"selfseg-a-self-supervised-sub-word","title":"SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation","date":"2023-07-31","arxiv_id":"2307.16400","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-supervised-learning-with","title":"Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training","date":"2023-07-17","arxiv_id":"2307.08416","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-aware-complex-valued-neural-machine","title":"Syntax-Aware Complex-Valued Neural Machine Translation","date":"2023-07-17","arxiv_id":"2307.08586","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-relaxed-optimization-approach-for-1","title":"A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models","date":"2023-06-14","arxiv_id":"2306.08492","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-translation-memory-augmented","title":"Rethinking Translation Memory Augmented Neural Machine Translation","date":"2023-06-12","arxiv_id":"2306.06948","repositories_listed":0,"syntology":null},{"url":null,"slug":"textual-augmentation-techniques-applied-to","title":"Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili","date":"2023-06-12","arxiv_id":"2306.07414","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-long-context-document-level-machine","title":"Improving Long Context Document-Level Machine Translation","date":"2023-06-08","arxiv_id":"2306.05183","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-search-strategies-for-document-level","title":"On Search Strategies for Document-Level Neural Machine Translation","date":"2023-06-08","arxiv_id":"2306.05116","repositories_listed":0,"syntology":null},{"url":null,"slug":"extract-and-attend-improving-entity","title":"Extract and Attend: Improving Entity Translation in Neural Machine Translation","date":"2023-06-04","arxiv_id":"2306.02242","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-does-pretraining-improve-discourse-aware","title":"How Does Pretraining Improve Discourse-Aware Translation?","date":"2023-05-31","arxiv_id":"2305.19847","repositories_listed":0,"syntology":null},{"url":null,"slug":"translation-enhanced-multilingual-text-to","title":"Translation-Enhanced Multilingual Text-to-Image Generation","date":"2023-05-30","arxiv_id":"2305.19216","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmenting-large-language-model-translators","title":"Augmenting Large Language Model Translators via Translation Memories","date":"2023-05-27","arxiv_id":"2305.17367","repositories_listed":0,"syntology":null},{"url":null,"slug":"codet-a-benchmark-for-contrastive-dialectal","title":"CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation","date":"2023-05-26","arxiv_id":"2305.17267","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-gpt-4-for-automatic-translation","title":"Leveraging GPT-4 for Automatic Translation Post-Editing","date":"2023-05-24","arxiv_id":"2305.14878","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-machine-translation-for-code","title":"Neural Machine Translation for Code Generation","date":"2023-05-22","arxiv_id":"2305.13504","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-by-projecting-text-into","title":"Machine Translation by Projecting Text into the Same Phonetic-Orthographic Space Using a Common Encoding","date":"2023-05-21","arxiv_id":"2305.12371","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-label-training-and-model-inertia-in","title":"Pseudo-Label Training and Model Inertia in Neural Machine Translation","date":"2023-05-19","arxiv_id":"2305.11808","repositories_listed":0,"syntology":null},{"url":null,"slug":"implications-of-multi-word-expressions-on","title":"Implications of Multi-Word Expressions on English to Bharti Braille Machine Translation","date":"2023-05-05","arxiv_id":"2305.06157","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-model-learning-for-various-neural","title":"Unified Model Learning for Various Neural Machine Translation","date":"2023-05-04","arxiv_id":"2305.02777","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-perception-adversarial-attacks-on","title":"Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems","date":"2023-05-02","arxiv_id":"2305.01437","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-autoregressive-nlp-tasks-via","title":"Improving Autoregressive NLP Tasks via Modular Linearized Attention","date":"2023-04-17","arxiv_id":"2304.08453","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-evaluation-of-the-copy","title":"A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions","date":"2023-04-16","arxiv_id":"2304.07772","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-homographic-disambiguation","title":"Learning Homographic Disambiguation Representation for Neural Machine Translation","date":"2023-04-12","arxiv_id":"2304.05860","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-neural-machine-translation-1","title":"Semi-supervised Neural Machine Translation with Consistency Regularization for Low-Resource Languages","date":"2023-04-02","arxiv_id":"2304.00557","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-multilingualism-in-low-resource","title":"Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning","date":"2023-03-31","arxiv_id":"2303.18011","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathcal-e-ku-mask-integrating-yoruba-cultural","title":"$\\varepsilon$ KÚ <MASK>: Integrating Yorùbá cultural greetings into machine translation","date":"2023-03-31","arxiv_id":"2303.17972","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-reliable-neural-machine-translation","title":"Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning","date":"2023-03-20","arxiv_id":"2303.10966","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-language-relatedness-in-machine","title":"Exploiting Language Relatedness in Machine Translation Through Domain Adaptation Techniques","date":"2023-03-03","arxiv_id":"2303.01793","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-analysis-of-vocabulary-and-bpe","title":"A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain Translation","date":"2023-03-01","arxiv_id":"2303.00722","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-nearest-neighbor-machine","title":"Federated Nearest Neighbor Machine Translation","date":"2023-02-23","arxiv_id":"2302.12211","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-algorithm-of-multimodal-pre","title":"Generalization algorithm of multimodal pre-training model based on graph-text self-supervised training","date":"2023-02-16","arxiv_id":"2302.10315","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-link-an-efficient-attention-based","title":"Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture","date":"2023-02-01","arxiv_id":"2302.00340","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-multi-stage-transitional","title":"A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation","date":"2023-01-27","arxiv_id":"2301.11749","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-machine-translation-with-phrase","title":"Improving Machine Translation with Phrase Pair Injection and Corpus Filtering","date":"2023-01-19","arxiv_id":"2301.08008","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompting-neural-machine-translation-with","title":"Prompting Neural Machine Translation with Translation Memories","date":"2023-01-13","arxiv_id":"2301.05380","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-a-parallel-corpus-and-training","title":"Building a Parallel Corpus and Training Translation Models Between Luganda and English","date":"2023-01-07","arxiv_id":"2301.02773","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-neural-networks-to-decompile","title":"Boosting Neural Networks to Decompile Optimized Binaries","date":"2023-01-03","arxiv_id":"2301.00969","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-pre-training-tasks-for-neural","title":"Synthetic Pre-Training Tasks for Neural Machine Translation","date":"2022-12-19","arxiv_id":"2212.09864","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-datastore-better-translation","title":"Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation","date":"2022-12-17","arxiv_id":"2212.08822","repositories_listed":0,"syntology":null},{"url":null,"slug":"who-evaluates-the-evaluators-on-automatic","title":"Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators","date":"2022-12-12","arxiv_id":"2212.06008","repositories_listed":0,"syntology":null},{"url":null,"slug":"dc-mbr-distributional-cooling-for-minimum","title":"DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding","date":"2022-12-08","arxiv_id":"2212.04205","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-alignment-in-the-era-of-deep-learning-a","title":"Word Alignment in the Era of Deep Learning: A Tutorial","date":"2022-11-30","arxiv_id":"2212.00138","repositories_listed":0,"syntology":null},{"url":null,"slug":"rank-one-editing-of-encoder-decoder-models","title":"Rank-One Editing of Encoder-Decoder Models","date":"2022-11-23","arxiv_id":"2211.13317","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-hallucinations-in-neural-machine","title":"Reducing Hallucinations in Neural Machine Translation with Feature Attribution","date":"2022-11-17","arxiv_id":"2211.09878","repositories_listed":0,"syntology":null},{"url":null,"slug":"hilmeme-a-human-in-the-loop-machine","title":"HilMeMe: A Human-in-the-Loop Machine Translation Evaluation Metric Looking into Multi-Word Expressions","date":"2022-11-09","arxiv_id":"2211.05201","repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-attention-forcing-for-machine","title":"Parallel Attention Forcing for Machine Translation","date":"2022-11-06","arxiv_id":"2211.03237","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-the-use-of-influence-functions-for","title":"Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation","date":"2022-10-24","arxiv_id":"2210.13281","repositories_listed":0,"syntology":null},{"url":null,"slug":"specializing-multi-domain-nmt-via-penalizing","title":"Specializing Multi-domain NMT via Penalizing Low Mutual Information","date":"2022-10-24","arxiv_id":"2210.12910","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semi-supervised-approach-for-a-better","title":"A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT","date":"2022-10-21","arxiv_id":"2210.11899","repositories_listed":0,"syntology":null},{"url":null,"slug":"sit-at-mixmt-2022-fluent-translation-built-on","title":"SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models","date":"2022-10-21","arxiv_id":"2210.11670","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-domains-be-transferred-across-languages","title":"Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?","date":"2022-10-20","arxiv_id":"2210.11628","repositories_listed":0,"syntology":null},{"url":null,"slug":"categorizing-semantic-representations-for-1","title":"Categorizing Semantic Representations for Neural Machine Translation","date":"2022-10-13","arxiv_id":"2210.06709","repositories_listed":0,"syntology":null},{"url":null,"slug":"dictdis-dictionary-constrained-disambiguation","title":"DICTDIS: Dictionary Constrained Disambiguation for Improved NMT","date":"2022-10-13","arxiv_id":"2210.06996","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-massive-multilingual-pre-trained","title":"Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning","date":"2022-10-12","arxiv_id":"2210.06068","repositories_listed":0,"syntology":null},{"url":null,"slug":"checks-and-strategies-for-enabling-code","title":"Checks and Strategies for Enabling Code-Switched Machine Translation","date":"2022-10-11","arxiv_id":"2210.05096","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-robustness-of-retrieval-augmented","title":"Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions","date":"2022-10-11","arxiv_id":"2210.05059","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-with-large-action-1","title":"Reinforcement Learning with Large Action Spaces for Neural Machine Translation","date":"2022-10-06","arxiv_id":"2210.03053","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-asymmetry-in-multilingual-neural","title":"Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"english-to-bengali-multimodal-neural-machine","title":"English to Bengali Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"featurebart-feature-based-sequence-to","title":"FeatureBART: Feature Based Sequence-to-Sequence Pre-Training for Low-Resource NMT","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hwtscsus-submissions-on-wat-2022-shared-task","title":"HwTscSU’s Submissions on WAT 2022 Shared Task","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"investigation-of-english-to-hindi-multimodal","title":"Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"d8f4785dce6a81277b1da2d5b3d69d41a316ac32e7e68abe2f26883ec1487dee","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}