{"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/machine-translation/papers/56","list_of":"/task/machine-translation","task":"Machine Translation","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":56,"pages_in_order":108,"rows_per_page":100,"rows":[5501,5600],"of":10752,"counts":{"archive_papers_tagged":10752,"with_a_code_link":2444,"where_syntology_ran_a_sample":477,"not_listed_spam_title":0,"listed":10752,"listed_where_code_ran":477,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":381,"every_run_a_failure_of_syntologys_instrument":96,"listed_with_a_run_with_no_instrument_failure":381,"listed_every_run_a_failure_of_syntologys_instrument":96,"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/machine-translation","prev":"/task/machine-translation/papers/55","next":"/task/machine-translation/papers/57","papers":[{"url":null,"slug":"learning-accurate-integer-transformer-machine","title":"Learning Accurate Integer Transformer Machine-Translation Models","date":"2020-01-03","arxiv_id":"2001.00926","repositories_listed":0,"syntology":null},{"url":null,"slug":"morphological-word-segmentation-on","title":"Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation","date":"2020-01-02","arxiv_id":"2001.01589","repositories_listed":0,"syntology":null},{"url":null,"slug":"text2sign-towards-sign-language-production","title":"Text2Sign: Towards Sign Language Production Using Neural Machine Translation and Generative Adversarial Networks","date":"2020-01-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-dimensional-self-attention-for","title":"Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"metagross-meta-gated-recursive-controller","title":"Metagross: Meta Gated Recursive Controller Units for Sequence Modeling","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reproducible-and-efficient-benchmarks-for","title":"Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"train-big-then-compress-rethinking-model-size","title":"Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"training-deep-networks-with-stochastic","title":"Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-agreement-regularized-training-for","title":"Visual Agreement Regularized Training for Multi-Modal Machine Translation","date":"2019-12-27","arxiv_id":"1912.12014","repositories_listed":0,"syntology":null},{"url":null,"slug":"amharic-arabic-neural-machine-translation","title":"Amharic-Arabic Neural Machine Translation","date":"2019-12-26","arxiv_id":"1912.13161","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-multilingual-neural-machine","title":"A Study of Multilingual Neural Machine Translation","date":"2019-12-25","arxiv_id":"1912.11625","repositories_listed":0,"syntology":null},{"url":"/paper/tag-less-back-translation","slug":"tag-less-back-translation","title":"Tag-less Back-Translation","date":"2019-12-22","arxiv_id":"1912.10514","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-dual-domain-adaptation-for-neural-1","title":"Iterative Dual Domain Adaptation for Neural Machine Translation","date":"2019-12-16","arxiv_id":"1912.07239","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-way-adversarial-unsupervised-word","title":"Two Way Adversarial Unsupervised Word Translation","date":"2019-12-12","arxiv_id":"1912.10168","repositories_listed":0,"syntology":null},{"url":null,"slug":"metamta-metalearning-method-leveraging","title":"MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation","date":"2019-12-11","arxiv_id":"1912.05467","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-future-sequence-of-actions-to","title":"Forecasting future action sequences with attention: a new approach to weakly supervised action forecasting","date":"2019-12-10","arxiv_id":"1912.04608","repositories_listed":0,"syntology":null},{"url":null,"slug":"homograph-disambiguation-through-selective-1","title":"Homograph Disambiguation Through Selective Diacritic Restoration","date":"2019-12-10","arxiv_id":"1912.04479","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-sensitive-training-for-autoregressive","title":"Cost-Sensitive Training for Autoregressive Models","date":"2019-12-08","arxiv_id":"1912.03771","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-network-embedding-coping-for-missing","title":"Document Network Embedding: Coping for Missing Content and Missing Links","date":"2019-12-06","arxiv_id":"1912.03048","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-sequence-level-knowledge","title":"Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation","date":"2019-12-06","arxiv_id":"1912.03334","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-evaluation-meets-1","title":"Machine Translation Evaluation Meets Community Question Answering","date":"2019-12-06","arxiv_id":"1912.02998","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-of-neural-machine-translation-in","title":"Exploration of Neural Machine Translation in Autoformalization of Mathematics in Mizar","date":"2019-12-05","arxiv_id":"1912.02636","repositories_listed":0,"syntology":null},{"url":null,"slug":"pairwise-neural-machine-translation-2","title":"Pairwise Neural Machine Translation Evaluation","date":"2019-12-05","arxiv_id":"1912.03135","repositories_listed":0,"syntology":null},{"url":null,"slug":"acquiring-knowledge-from-pre-trained-model-to","title":"Acquiring Knowledge from Pre-trained Model to Neural Machine Translation","date":"2019-12-04","arxiv_id":"1912.01774","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-pre-training-based-transfer-for","title":"Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation","date":"2019-12-03","arxiv_id":"1912.01214","repositories_listed":0,"syntology":null},{"url":null,"slug":"audiovisual-transformer-architectures-for","title":"Audiovisual Transformer Architectures for Large-Scale Classification and Synchronization of Weakly Labeled Audio Events","date":"2019-12-02","arxiv_id":"1912.02615","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-bootstrapping-using-neural","title":"Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition","date":"2019-12-02","arxiv_id":"1912.00958","repositories_listed":0,"syntology":null},{"url":null,"slug":"merging-external-bilingual-pairs-into-neural","title":"Merging External Bilingual Pairs into Neural Machine Translation","date":"2019-12-02","arxiv_id":"1912.00567","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-unsupervised-word-translation","title":"Comparing Unsupervised Word Translation Methods Step by Step","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-de-attention-networks","title":"Compositional De-Attention Networks","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kunji-a-resource-management-system-for-higher","title":"Kunji : A Resource Management System for Higher Productivity in Computer Aided Translation Tools","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"language-modelling-with-nmt-query-translation","title":"Language Modelling with NMT Query Translation for Amharic-Arabic Cross-Language Information Retrieval","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mixtape-breaking-the-softmax-bottleneck","title":"Mixtape: Breaking the Softmax Bottleneck Efficiently","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-text-classification-using-sub-word","title":"Robust Text Classification using Sub-Word Information in Input Word Representations.","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-handling-verb-phrase-ellipsis-in","title":"Towards Handling Verb Phrase Ellipsis in English-Hindi Machine Translation","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discotk-using-discourse-structure-for-machine-1","title":"DiscoTK: Using Discourse Structure for Machine Translation Evaluation","date":"2019-11-28","arxiv_id":"1911.12547","repositories_listed":0,"syntology":null},{"url":"/paper/multimodal-machine-translation-through","slug":"multimodal-machine-translation-through","title":"Multimodal Machine Translation through Visuals and Speech","date":"2019-11-28","arxiv_id":"1911.12798","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-neural-machine-translation-using","title":"Simultaneous Neural Machine Translation using Connectionist Temporal Classification","date":"2019-11-27","arxiv_id":"1911.11933","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-batch-back-translation-for-neural","title":"Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model","date":"2019-11-26","arxiv_id":"2001.11327","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-machine-translation-with-explicit","title":"Neural Machine Translation with Explicit Phrase Alignment","date":"2019-11-26","arxiv_id":"1911.11520","repositories_listed":0,"syntology":null},{"url":null,"slug":"hauwe-hausa-words-embedding-for-natural","title":"hauWE: Hausa Words Embedding for Natural Language Processing","date":"2019-11-25","arxiv_id":"1911.10708","repositories_listed":0,"syntology":null},{"url":"/paper/jparacrawl-a-large-scale-web-based-english","slug":"jparacrawl-a-large-scale-web-based-english","title":"JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus","date":"2019-11-25","arxiv_id":"1911.10668","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-reuse-translations-guiding-neural","title":"Learning to Reuse Translations: Guiding Neural Machine Translation with Examples","date":"2019-11-25","arxiv_id":"1911.10732","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-autoregressive-transformer-by-position","title":"Non-autoregressive Transformer by Position Learning","date":"2019-11-25","arxiv_id":"1911.10677","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-the-web-as-an-implicit-training-set","title":"Using the Web as an Implicit Training Set: Application to Noun Compound Syntax and Semantics","date":"2019-11-23","arxiv_id":"1912.01113","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuron-interaction-based-representation","title":"Neuron Interaction Based Representation Composition for Neural Machine Translation","date":"2019-11-22","arxiv_id":"1911.09877","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-diverse-translation-by","title":"Generating Diverse Translation by Manipulating Multi-Head Attention","date":"2019-11-21","arxiv_id":"1911.09333","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-morpheme-word-representation-for","title":"A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages","date":"2019-11-19","arxiv_id":"1911.08117","repositories_listed":0,"syntology":null},{"url":null,"slug":"dlbricks-composable-benchmark-generation","title":"DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)","date":"2019-11-18","arxiv_id":"1911.07967","repositories_listed":0,"syntology":null},{"url":null,"slug":"contribution-au-niveau-de-lapproche-indirecte","title":"Contribution au Niveau de l'Approche Indirecte à Base de Transfert dans la Traduction Automatique","date":"2019-11-16","arxiv_id":"1911.07030","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-mini-batch-size-selection-for-fast","title":"Optimal Mini-Batch Size Selection for Fast Gradient Descent","date":"2019-11-15","arxiv_id":"1911.06459","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-direct-speech-to-text","title":"Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning","date":"2019-11-11","arxiv_id":"1911.04283","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversity-by-phonetics-and-its-application-in","title":"Diversity by Phonetics and its Application in Neural Machine Translation","date":"2019-11-11","arxiv_id":"1911.04292","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-fusion-for-multimodal-data","title":"Adaptive Fusion Techniques for Multimodal Data","date":"2019-11-10","arxiv_id":"1911.03821","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-driven-unsupervised-neural","title":"Language Model-Driven Unsupervised Neural Machine Translation","date":"2019-11-10","arxiv_id":"1911.03937","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-bahdanau-attention-using-election","title":"Modelling Bahdanau Attention using Election methods aided by Q-Learning","date":"2019-11-10","arxiv_id":"1911.03853","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-autoregressive-transformer-automatic","title":"Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition","date":"2019-11-10","arxiv_id":"1911.04908","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-infused-transformer-and-bert-models","title":"Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding","date":"2019-11-10","arxiv_id":"1911.06156","repositories_listed":0,"syntology":null},{"url":null,"slug":"translationese-as-a-language-in-multilingual","title":"Translationese as a Language in \"Multilingual\" NMT","date":"2019-11-10","arxiv_id":"1911.03823","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-multi-head-attention-in","title":"Understanding Multi-Head Attention in Abstractive Summarization","date":"2019-11-10","arxiv_id":"1911.03898","repositories_listed":0,"syntology":null},{"url":null,"slug":"bootstrapping-disjoint-datasets-for","title":"Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning","date":"2019-11-09","arxiv_id":"1911.03678","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-deep-learning-inference-on","title":"Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection","date":"2019-11-09","arxiv_id":"1911.04946","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-paraphrase-generation-with","title":"Zero-Shot Paraphrase Generation with Multilingual Language Models","date":"2019-11-09","arxiv_id":"1911.03597","repositories_listed":0,"syntology":null},{"url":"/paper/europarl-st-a-multilingual-corpus-for-speech","slug":"europarl-st-a-multilingual-corpus-for-speech","title":"Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates","date":"2019-11-08","arxiv_id":"1911.03167","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-do-simultaneous-translation-better","title":"A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation","date":"2019-11-08","arxiv_id":"1911.03154","repositories_listed":0,"syntology":null},{"url":null,"slug":"pretrained-language-models-for-document-level","title":"Pretrained Language Models for Document-Level Neural Machine Translation","date":"2019-11-08","arxiv_id":"1911.03110","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-lstm-performance-through-dynamic","title":"Boosting LSTM Performance Through Dynamic Precision Selection","date":"2019-11-07","arxiv_id":"1911.04244","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-neural-networks-learn-symbolic-rewriting","title":"Can Neural Networks Learn Symbolic Rewriting?","date":"2019-11-07","arxiv_id":"1911.04873","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-resource-machine-translation-using","title":"Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation","date":"2019-11-07","arxiv_id":"1911.02709","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-contextualized-sentence","title":"Probing Contextualized Sentence Representations with Visual Awareness","date":"2019-11-07","arxiv_id":"1911.02971","repositories_listed":0,"syntology":null},{"url":null,"slug":"subcharacter-chinese-english-neural-machine","title":"SubCharacter Chinese-English Neural Machine Translation with Wubi encoding","date":"2019-11-07","arxiv_id":"1911.02737","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-lig-system-for-the-english-czech-text","title":"The LIG system for the English-Czech Text Translation Task of IWSLT 2019","date":"2019-11-07","arxiv_id":"1911.02898","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-knowledge-distillation-in-non","title":"Understanding Knowledge Distillation in Non-autoregressive Machine Translation","date":"2019-11-07","arxiv_id":"1911.02727","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-translationese-and-noise-in-synthetic","title":"Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation","date":"2019-11-06","arxiv_id":"1911.03362","repositories_listed":0,"syntology":null},{"url":null,"slug":"guiding-non-autoregressive-neural-machine","title":"Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information","date":"2019-11-06","arxiv_id":"1911.02215","repositories_listed":0,"syntology":null},{"url":null,"slug":"biconditional-generative-adversarial-networks","title":"Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views","date":"2019-11-05","arxiv_id":"1911.01861","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-bidirectional-decoding-with-dynamic","title":"Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation","date":"2019-11-05","arxiv_id":"1911.01597","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-dictionary-feature-into-a-deep","title":"Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition","date":"2019-11-05","arxiv_id":"1911.01600","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-neural-machine-translation-nmt","title":"Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F)","date":"2019-11-05","arxiv_id":"1911.01933","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-coreference-in-transformer-outputs-1","title":"Analysing Coreference in Transformer Outputs","date":"2019-11-04","arxiv_id":"1911.01188","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-compositionality-in-neural-machine","title":"On Compositionality in Neural Machine Translation","date":"2019-11-04","arxiv_id":"1911.01497","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-in-pronunciation-space","title":"Machine Translation in Pronunciation Space","date":"2019-11-03","arxiv_id":"1911.00932","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-evaluation-using-bi","title":"Machine Translation Evaluation using Bi-directional Entailment","date":"2019-11-02","arxiv_id":"1911.00681","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-margin-based-loss-with-synthetic-negative","title":"A Margin-based Loss with Synthetic Negative Samples for Continuous-output Machine Translation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-modular-architecture-for-unsupervised","title":"A Modular Architecture for Unsupervised Sarcasm Generation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-precision-training-quantify-back","title":"Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers","date":"2019-11-01","arxiv_id":"1911.00361","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptively-scheduled-multitask-learning-the","title":"Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-positional-encodings-for-neural","title":"Analysis of Positional Encodings for Neural Machine Translation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatically-extracting-challenge-sets-for-1","title":"Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"back-translation-as-strategy-to-tackle-the","title":"Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"benefits-of-data-augmentation-for-nmt-based","title":"Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cmus-machine-translation-system-for-iwslt","title":"CMU’s Machine Translation System for IWSLT 2019","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-global-sparse-gradients-with-local","title":"Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-translation-memory-with-neural","title":"Combining Translation Memory with Neural Machine Translation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compact-and-robust-models-for-japanese","title":"Compact and Robust Models for Japanese-English Character-level Machine Translation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-neural-machine-translation-2","title":"Context-Aware Neural Machine Translation Decoding","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-neural-machine-translation-with","title":"Context-aware Neural Machine Translation with Coreference Information","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"controlling-japanese-honorifics-in-english-to","title":"Controlling Japanese Honorifics in English-to-Japanese Neural Machine Translation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-task-knowledge-transfer-for-query-based","title":"Cross-Task Knowledge Transfer for Query-Based Text Summarization","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cvits-submissions-to-wat-2019","title":"CVIT's submissions to WAT-2019","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"0c93d905aba7127e840af9ac6698c3f930c1582613275cafff4b473814d91bb8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}