{"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/23","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":23,"pages_in_order":108,"rows_per_page":100,"rows":[2201,2300],"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/22","next":"/task/machine-translation/papers/24","papers":[{"url":"/paper/token-level-and-sequence-level-loss-smoothing","slug":"token-level-and-sequence-level-loss-smoothing","title":"Token-level and sequence-level loss smoothing for RNN language models","date":"2018-05-14","arxiv_id":"1805.05062","repositories_listed":1,"syntology":null},{"url":"/paper/bag-of-words-as-target-for-neural-machine","slug":"bag-of-words-as-target-for-neural-machine","title":"Bag-of-Words as Target for Neural Machine Translation","date":"2018-05-13","arxiv_id":"1805.04871","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-estimation-of-simultaneous","slug":"automatic-estimation-of-simultaneous","title":"Automatic Estimation of Simultaneous Interpreter Performance","date":"2018-05-10","arxiv_id":"1805.04016","repositories_listed":1,"syntology":null},{"url":"/paper/deep-neural-machine-translation-with-weakly","slug":"deep-neural-machine-translation-with-weakly","title":"Deep Neural Machine Translation with Weakly-Recurrent Units","date":"2018-05-10","arxiv_id":"1805.04185","repositories_listed":1,"syntology":null},{"url":"/paper/character-level-chinese-english-translation","slug":"character-level-chinese-english-translation","title":"Character-level Chinese-English Translation through ASCII Encoding","date":"2018-05-09","arxiv_id":"1805.03330","repositories_listed":1,"syntology":null},{"url":"/paper/stay-on-topic-generating-context-specific","slug":"stay-on-topic-generating-context-specific","title":"Stay On-Topic: Generating Context-specific Fake Restaurant Reviews","date":"2018-05-07","arxiv_id":"1805.02400","repositories_listed":1,"syntology":null},{"url":"/paper/extreme-adaptation-for-personalized-neural","slug":"extreme-adaptation-for-personalized-neural","title":"Extreme Adaptation for Personalized Neural Machine Translation","date":"2018-05-04","arxiv_id":"1805.01817","repositories_listed":1,"syntology":null},{"url":"/paper/upping-the-ante-towards-a-better-benchmark","slug":"upping-the-ante-towards-a-better-benchmark","title":"Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation","date":"2018-05-04","arxiv_id":"1805.01676","repositories_listed":1,"syntology":null},{"url":"/paper/a-reinforcement-learning-approach-to","slug":"a-reinforcement-learning-approach-to","title":"A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation","date":"2018-05-03","arxiv_id":"1805.01553","repositories_listed":1,"syntology":null},{"url":"/paper/apply-chinese-radicals-into-neural-machine","slug":"apply-chinese-radicals-into-neural-machine","title":"Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character Level","date":"2018-05-03","arxiv_id":"1805.01565","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-neural-transformer-via-an","slug":"accelerating-neural-transformer-via-an","title":"Accelerating Neural Transformer via an Average Attention Network","date":"2018-05-02","arxiv_id":"1805.00631","repositories_listed":1,"syntology":{"n":19,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":12,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/accelerating-neural-transformer-via-an#ran","syntology_url":"https://syntology.ai/paper/1805.00631","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.00631"}},"official":{"repos":["bzhangXMU/transformer-aan"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":12,"ran_from_kinds":["official"]}}},{"url":"/paper/a-workbench-for-rapid-generation-of-cross","slug":"a-workbench-for-rapid-generation-of-cross","title":"A Workbench for Rapid Generation of Cross-Lingual Summaries","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/auto-hmds-automatic-construction-of-a-large","slug":"auto-hmds-automatic-construction-of-a-large","title":"Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bilstm-crf-for-persian-named-entity","slug":"bilstm-crf-for-persian-named-entity","title":"BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/building-named-entity-recognition-taggers-via","slug":"building-named-entity-recognition-taggers-via","title":"Building Named Entity Recognition Taggers via Parallel Corpora","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/creating-a-translation-matrix-of-the-bibleas","slug":"creating-a-translation-matrix-of-the-bibleas","title":"Creating a Translation Matrix of the Bible's Names Across 591 Languages","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/creating-large-scale-multilingual-cognate","slug":"creating-large-scale-multilingual-cognate","title":"Creating Large-Scale Multilingual Cognate Tables","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/massively-translingual-compound-analysis-and","slug":"massively-translingual-compound-analysis-and","title":"Massively Translingual Compound Analysis and Translation Discovery","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mgad-multilingual-generation-of-analogy","slug":"mgad-multilingual-generation-of-analogy","title":"MGAD: Multilingual Generation of Analogy Datasets","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-lexical-translation","slug":"multimodal-lexical-translation","title":"Multimodal Lexical Translation","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/parallel-corpora-for-the-biomedical-domain","slug":"parallel-corpora-for-the-biomedical-domain","title":"Parallel Corpora for the Biomedical Domain","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/someweta-a-part-of-speech-tagger-for-german","slug":"someweta-a-part-of-speech-tagger-for-german","title":"SoMeWeTa: A Part-of-Speech Tagger for German Social Media and Web Texts","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tf-lm-tensorflow-based-language-modeling","slug":"tf-lm-tensorflow-based-language-modeling","title":"TF-LM: TensorFlow-based Language Modeling Toolkit","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/training-and-adapting-multilingual-nmt-for","slug":"training-and-adapting-multilingual-nmt-for","title":"Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/two-multilingual-corpora-extracted-from-the","slug":"two-multilingual-corpora-extracted-from-the","title":"Two Multilingual Corpora Extracted from the Tenders Electronic Daily for Machine Learning and Machine Translation Applications.","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/word-embedding-approach-for-synonym","slug":"word-embedding-approach-for-synonym","title":"Word Embedding Approach for Synonym Extraction of Multi-Word Terms","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/syllable-based-sequence-to-sequence-speech","slug":"syllable-based-sequence-to-sequence-speech","title":"Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese","date":"2018-04-28","arxiv_id":"1804.10752","repositories_listed":1,"syntology":null},{"url":"/paper/optimus-an-efficient-dynamic-resource","slug":"optimus-an-efficient-dynamic-resource","title":"Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning Clusters","date":"2018-04-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-the-evaluation-of-semantic-phenomena-in","slug":"on-the-evaluation-of-semantic-phenomena-in","title":"On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference","date":"2018-04-25","arxiv_id":"1804.09779","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-neural-machine-translation-with-1","slug":"unsupervised-neural-machine-translation-with-1","title":"Unsupervised Neural Machine Translation with Weight Sharing","date":"2018-04-24","arxiv_id":"1804.09057","repositories_listed":1,"syntology":null},{"url":"/paper/a-stable-and-effective-learning-strategy-for","slug":"a-stable-and-effective-learning-strategy-for","title":"A Stable and Effective Learning Strategy for Trainable Greedy Decoding","date":"2018-04-21","arxiv_id":"1804.07915","repositories_listed":1,"syntology":null},{"url":"/paper/when-and-why-are-pre-trained-word-embeddings","slug":"when-and-why-are-pre-trained-word-embeddings","title":"When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?","date":"2018-04-17","arxiv_id":"1804.06323","repositories_listed":1,"syntology":null},{"url":"/paper/approaching-neural-grammatical-error","slug":"approaching-neural-grammatical-error","title":"Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task","date":"2018-04-16","arxiv_id":"1804.05940","repositories_listed":1,"syntology":null},{"url":"/paper/attentive-sequence-to-sequence-learning-for","slug":"attentive-sequence-to-sequence-learning-for","title":"Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language Text","date":"2018-04-03","arxiv_id":"1804.00832","repositories_listed":1,"syntology":null},{"url":"/paper/identifying-semantic-divergences-in-parallel","slug":"identifying-semantic-divergences-in-parallel","title":"Identifying Semantic Divergences in Parallel Text without Annotations","date":"2018-03-29","arxiv_id":"1803.11112","repositories_listed":1,"syntology":null},{"url":"/paper/demystifying-differentiable-programming","slug":"demystifying-differentiable-programming","title":"Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator","date":"2018-03-27","arxiv_id":"1803.10228","repositories_listed":1,"syntology":{"n":14,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/demystifying-differentiable-programming#ran","syntology_url":"https://syntology.ai/paper/1803.10228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10228"}},"official":{"repos":["feiwang3311/Lantern"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/dear-sir-or-madam-may-i-introduce-the-gyafc","slug":"dear-sir-or-madam-may-i-introduce-the-gyafc","title":"Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer","date":"2018-03-17","arxiv_id":"1803.06535","repositories_listed":1,"syntology":null},{"url":"/paper/the-importance-of-being-recurrent-for","slug":"the-importance-of-being-recurrent-for","title":"The Importance of Being Recurrent for Modeling Hierarchical Structure","date":"2018-03-09","arxiv_id":"1803.03585","repositories_listed":1,"syntology":null},{"url":"/paper/seq2sick-evaluating-the-robustness-of","slug":"seq2sick-evaluating-the-robustness-of","title":"Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples","date":"2018-03-03","arxiv_id":"1803.01128","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/seq2sick-evaluating-the-robustness-of#ran","syntology_url":"https://syntology.ai/paper/1803.01128","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.01128"}},"official":{"repos":["cmhcbb/Seq2Sick"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/the-sockeye-neural-machine-translation","slug":"the-sockeye-neural-machine-translation","title":"The Sockeye Neural Machine Translation Toolkit at AMTA 2018","date":"2018-03-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/xnmt-the-extensible-neural-machine","slug":"xnmt-the-extensible-neural-machine","title":"XNMT: The eXtensible Neural Machine Translation Toolkit","date":"2018-03-01","arxiv_id":"1803.00188","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-uncertainty-in-neural-machine","slug":"analyzing-uncertainty-in-neural-machine","title":"Analyzing Uncertainty in Neural Machine Translation","date":"2018-02-28","arxiv_id":"1803.00047","repositories_listed":1,"syntology":null},{"url":"/paper/amuse-multilingual-semantic-parsing-for","slug":"amuse-multilingual-semantic-parsing-for","title":"AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data","date":"2018-02-26","arxiv_id":"1802.09296","repositories_listed":1,"syntology":null},{"url":"/paper/cytonmt-an-efficient-neural-machine","slug":"cytonmt-an-efficient-neural-machine","title":"CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++","date":"2018-02-17","arxiv_id":"1802.07170","repositories_listed":1,"syntology":null},{"url":"/paper/from-gameplay-to-symbolic-reasoning-learning","slug":"from-gameplay-to-symbolic-reasoning-learning","title":"From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero","date":"2018-02-14","arxiv_id":"1802.05340","repositories_listed":1,"syntology":null},{"url":"/paper/examining-the-tip-of-the-iceberg-a-data-set","slug":"examining-the-tip-of-the-iceberg-a-data-set","title":"Examining the Tip of the Iceberg: A Data Set for Idiom Translation","date":"2018-02-13","arxiv_id":"1802.04681","repositories_listed":1,"syntology":null},{"url":"/paper/online-learning-for-effort-reduction-in","slug":"online-learning-for-effort-reduction-in","title":"Online Learning for Effort Reduction in Interactive Neural Machine Translation","date":"2018-02-10","arxiv_id":"1802.03594","repositories_listed":1,"syntology":null},{"url":"/paper/augmenting-librispeech-with-french","slug":"augmenting-librispeech-with-french","title":"Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation","date":"2018-02-09","arxiv_id":"1802.03142","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-neural-network-based-semantic","slug":"recurrent-neural-network-based-semantic","title":"Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning","date":"2018-02-09","arxiv_id":"1802.03238","repositories_listed":1,"syntology":null},{"url":"/paper/quantitative-fine-grained-human-evaluation-of","slug":"quantitative-fine-grained-human-evaluation-of","title":"Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian","date":"2018-02-02","arxiv_id":"1802.01451","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-programming","slug":"deep-reinforcement-learning-for-programming","title":"Deep Reinforcement Learning for Programming Language Correction","date":"2018-01-31","arxiv_id":"1801.10467","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-layers-of-representation-in-neural","slug":"evaluating-layers-of-representation-in-neural","title":"Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks","date":"2018-01-23","arxiv_id":"1801.07772","repositories_listed":1,"syntology":null},{"url":"/paper/a-resource-light-method-for-cross-lingual","slug":"a-resource-light-method-for-cross-lingual","title":"A Resource-Light Method for Cross-Lingual Semantic Textual Similarity","date":"2018-01-19","arxiv_id":"1801.06436","repositories_listed":1,"syntology":null},{"url":"/paper/image-captioning-using-deep-neural","slug":"image-captioning-using-deep-neural","title":"Image Captioning using Deep Neural Architectures","date":"2018-01-17","arxiv_id":"1801.05568","repositories_listed":1,"syntology":null},{"url":"/paper/translating-pro-drop-languages-with","slug":"translating-pro-drop-languages-with","title":"Translating Pro-Drop Languages with Reconstruction Models","date":"2018-01-10","arxiv_id":"1801.03257","repositories_listed":1,"syntology":null},{"url":"/paper/mizan-a-large-persian-english-parallel-corpus","slug":"mizan-a-large-persian-english-parallel-corpus","title":"MIZAN: A Large Persian-English Parallel Corpus","date":"2018-01-07","arxiv_id":"1801.02107","repositories_listed":1,"syntology":null},{"url":"/paper/charcut-human-targeted-character-based-mt","slug":"charcut-human-targeted-character-based-mt","title":"CHARCUT: Human-Targeted Character-Based MT Evaluation with Loose Differences","date":"2017-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/monolingual-embeddings-for-low-resourced","slug":"monolingual-embeddings-for-low-resourced","title":"Monolingual Embeddings for Low Resourced Neural Machine Translation","date":"2017-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/modeling-past-and-future-for-neural-machine","slug":"modeling-past-and-future-for-neural-machine","title":"Modeling Past and Future for Neural Machine Translation","date":"2017-11-27","arxiv_id":"1711.09502","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-remember-translation-history-with","slug":"learning-to-remember-translation-history-with","title":"Learning to Remember Translation History with a Continuous Cache","date":"2017-11-26","arxiv_id":"1711.09367","repositories_listed":1,"syntology":null},{"url":"/paper/effective-strategies-in-zero-shot-neural","slug":"effective-strategies-in-zero-shot-neural","title":"Effective Strategies in Zero-Shot Neural Machine Translation","date":"2017-11-21","arxiv_id":"1711.07893","repositories_listed":1,"syntology":null},{"url":"/paper/using-stochastic-computation-graphs-formalism","slug":"using-stochastic-computation-graphs-formalism","title":"Using stochastic computation graphs formalism for optimization of sequence-to-sequence model","date":"2017-11-21","arxiv_id":"1711.07724","repositories_listed":1,"syntology":null},{"url":"/paper/classical-structured-prediction-losses-for","slug":"classical-structured-prediction-losses-for","title":"Classical Structured Prediction Losses for Sequence to Sequence Learning","date":"2017-11-14","arxiv_id":"1711.04956","repositories_listed":1,"syntology":null},{"url":"/paper/word-subword-or-character-an-empirical-study","slug":"word-subword-or-character-an-empirical-study","title":"Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT","date":"2017-11-13","arxiv_id":"1711.04457","repositories_listed":1,"syntology":null},{"url":"/paper/a-bag-of-useful-tricks-for-practical-neural","slug":"a-bag-of-useful-tricks-for-practical-neural","title":"A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size","date":"2017-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deriving-consensus-for-multi-parallel-corpora","slug":"deriving-consensus-for-multi-parallel-corpora","title":"Deriving Consensus for Multi-Parallel Corpora: an English Bible Study","date":"2017-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-low-resource-neural-machine","slug":"improving-low-resource-neural-machine","title":"Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel Corpus","date":"2017-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/kyoto-university-participation-to-wat-2017","slug":"kyoto-university-participation-to-wat-2017","title":"Kyoto University Participation to WAT 2017","date":"2017-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-how-to-simplify-from-explicit","slug":"learning-how-to-simplify-from-explicit","title":"Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs","date":"2017-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generating-natural-adversarial-examples","slug":"generating-natural-adversarial-examples","title":"Generating Natural Adversarial Examples","date":"2017-10-31","arxiv_id":"1710.11342","repositories_listed":1,"syntology":null},{"url":"/paper/machine-translation-of-low-resource-spoken","slug":"machine-translation-of-low-resource-spoken","title":"Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German","date":"2017-10-30","arxiv_id":"1710.11035","repositories_listed":1,"syntology":null},{"url":"/paper/a-dual-encoder-sequence-to-sequence-model-for","slug":"a-dual-encoder-sequence-to-sequence-model-for","title":"A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue Modeling","date":"2017-10-28","arxiv_id":"1710.10520","repositories_listed":1,"syntology":null},{"url":"/paper/paying-attention-to-multi-word-expressions-in","slug":"paying-attention-to-multi-word-expressions-in","title":"Paying Attention to Multi-Word Expressions in Neural Machine Translation","date":"2017-10-17","arxiv_id":"1710.06313","repositories_listed":1,"syntology":null},{"url":"/paper/self-attentive-residual-decoder-for-neural","slug":"self-attentive-residual-decoder-for-neural","title":"Self-Attentive Residual Decoder for Neural Machine Translation","date":"2017-09-14","arxiv_id":"1709.04849","repositories_listed":1,"syntology":null},{"url":"/paper/a-multilingual-parallel-corpus-for-improving","slug":"a-multilingual-parallel-corpus-for-improving","title":"A Multilingual Parallel Corpus for Improving Machine Translation on Southeast Asian Languages","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adapting-neural-machine-translation-with","slug":"adapting-neural-machine-translation-with","title":"Adapting Neural Machine Translation with Parallel Synthetic Data","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/c-3ma-tartu-riga-zurich-translation-systems","slug":"c-3ma-tartu-riga-zurich-translation-systems","title":"C-3MA: Tartu-Riga-Zurich Translation Systems for WMT17","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-telicity-using-cross","slug":"classification-of-telicity-using-cross","title":"Classification of telicity using cross-linguistic annotation projection","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dict2vec-learning-word-embeddings-using","slug":"dict2vec-learning-word-embeddings-using","title":"Dict2vec : Learning Word Embeddings using Lexical Dictionaries","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-the-morphological-competence-of","slug":"evaluating-the-morphological-competence-of","title":"Evaluating the morphological competence of Machine Translation Systems","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/further-investigation-into-reference-bias-in","slug":"further-investigation-into-reference-bias-in","title":"Further Investigation into Reference Bias in Monolingual Evaluation of Machine Translation","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/instance-weighting-for-neural-machine","slug":"instance-weighting-for-neural-machine","title":"Instance Weighting for Neural Machine Translation Domain Adaptation","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/same-same-but-different-compositionality-of","slug":"same-same-but-different-compositionality-of","title":"Same same, but different: Compositionality of paraphrase granularity levels","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/systematically-adapting-machine-translation","slug":"systematically-adapting-machine-translation","title":"Systematically Adapting Machine Translation for Grammatical Error Correction","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-jaist-machine-translation-systems-for-wmt","slug":"the-jaist-machine-translation-systems-for-wmt","title":"The JAIST Machine Translation Systems for WMT 17","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tildes-machine-translation-systems-for-wmt-1","slug":"tildes-machine-translation-systems-for-wmt-1","title":"Tilde's Machine Translation Systems for WMT 2017","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/using-hyperlinks-to-improve-multilingual","slug":"using-hyperlinks-to-improve-multilingual","title":"Using hyperlinks to improve multilingual partial parsers","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/using-target-side-monolingual-data-for-neural","slug":"using-target-side-monolingual-data-for-neural","title":"Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/validation-of-an-automatic-metric-for-the","slug":"validation-of-an-automatic-metric-for-the","title":"Validation of an Automatic Metric for the Accuracy of Pronoun Translation (APT)","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-helsinki-neural-machine-translation","slug":"the-helsinki-neural-machine-translation","title":"The Helsinki Neural Machine Translation System","date":"2017-08-20","arxiv_id":"1708.05942","repositories_listed":1,"syntology":null},{"url":"/paper/natural-language-processing-state-of-the-art","slug":"natural-language-processing-state-of-the-art","title":"Natural Language Processing: State of The Art, Current Trends and Challenges","date":"2017-08-17","arxiv_id":"1708.05148","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-entity-alignment-via-joint","slug":"cross-lingual-entity-alignment-via-joint","title":"Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding","date":"2017-08-16","arxiv_id":"1708.05045","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cross-lingual-entity-alignment-via-joint#ran","syntology_url":"https://syntology.ai/paper/1708.05045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.05045"}},"official":{"repos":["nju-websoft/JAPE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-data-selection-for-neural-machine","slug":"dynamic-data-selection-for-neural-machine","title":"Dynamic Data Selection for Neural Machine Translation","date":"2017-08-02","arxiv_id":"1708.00712","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamic-data-selection-for-neural-machine#ran","syntology_url":"https://syntology.ai/paper/1708.00712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.00712"}},"official":{"repos":["marliesvanderwees/dds-nmt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/adapting-sequence-models-for-sentence","slug":"adapting-sequence-models-for-sentence","title":"Adapting Sequence Models for Sentence Correction","date":"2017-07-27","arxiv_id":"1707.09067","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-for-bandit-neural","slug":"reinforcement-learning-for-bandit-neural","title":"Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback","date":"2017-07-24","arxiv_id":"1707.07402","repositories_listed":1,"syntology":null},{"url":"/paper/sgnmt-a-flexible-nmt-decoding-platform-for","slug":"sgnmt-a-flexible-nmt-decoding-platform-for","title":"SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies","date":"2017-07-21","arxiv_id":"1707.06885","repositories_listed":1,"syntology":null},{"url":"/paper/improved-neural-machine-translation-with-a","slug":"improved-neural-machine-translation-with-a","title":"Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder","date":"2017-07-18","arxiv_id":"1707.05436","repositories_listed":1,"syntology":null},{"url":"/paper/lium-machine-translation-systems-for-wmt17","slug":"lium-machine-translation-systems-for-wmt17","title":"LIUM Machine Translation Systems for WMT17 News Translation Task","date":"2017-07-14","arxiv_id":"1707.04499","repositories_listed":1,"syntology":null},{"url":"/paper/an-embedded-deep-learning-based-word","slug":"an-embedded-deep-learning-based-word","title":"An Embedded Deep Learning based Word Prediction","date":"2017-07-06","arxiv_id":"1707.01662","repositories_listed":1,"syntology":null},{"url":"/paper/single-queue-decoding-for-neural-machine","slug":"single-queue-decoding-for-neural-machine","title":"Single-Queue Decoding for Neural Machine Translation","date":"2017-07-06","arxiv_id":"1707.01830","repositories_listed":1,"syntology":null}],"record_sha256":"238d6e0d56f67d52ae34c9e5f22c1cc6474670d3437007200d26cba7f6dbf91f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}