{"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/18","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":18,"pages_in_order":108,"rows_per_page":100,"rows":[1701,1800],"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/17","next":"/task/machine-translation/papers/19","papers":[{"url":"/paper/scene-graph-modification-based-on-natural","slug":"scene-graph-modification-based-on-natural","title":"Scene Graph Modification Based on Natural Language Commands","date":"2020-10-06","arxiv_id":"2010.02591","repositories_listed":1,"syntology":null},{"url":"/paper/a-streaming-approach-for-efficient-batched","slug":"a-streaming-approach-for-efficient-batched","title":"A Streaming Approach For Efficient Batched Beam Search","date":"2020-10-05","arxiv_id":"2010.02164","repositories_listed":1,"syntology":null},{"url":"/paper/improving-amr-parsing-with-sequence-to","slug":"improving-amr-parsing-with-sequence-to","title":"Improving AMR Parsing with Sequence-to-Sequence Pre-training","date":"2020-10-05","arxiv_id":"2010.01771","repositories_listed":1,"syntology":null},{"url":"/paper/we-don-t-speak-the-same-language-interpreting","slug":"we-don-t-speak-the-same-language-interpreting","title":"We Don't Speak the Same Language: Interpreting Polarization through Machine Translation","date":"2020-10-05","arxiv_id":"2010.02339","repositories_listed":1,"syntology":null},{"url":"/paper/x-srl-a-parallel-cross-lingual-semantic-role","slug":"x-srl-a-parallel-cross-lingual-semantic-role","title":"X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset","date":"2020-10-05","arxiv_id":"2010.01998","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-multilingual-news-websites-for","slug":"leveraging-multilingual-news-websites-for","title":"Leveraging Multilingual News Websites for Building a Kurdish Parallel Corpus","date":"2020-10-04","arxiv_id":"2010.01554","repositories_listed":1,"syntology":null},{"url":"/paper/can-automatic-post-editing-improve-nmt","slug":"can-automatic-post-editing-improve-nmt","title":"Can Automatic Post-Editing Improve NMT?","date":"2020-09-30","arxiv_id":"2009.14395","repositories_listed":1,"syntology":null},{"url":"/paper/deep-transformers-with-latent-depth","slug":"deep-transformers-with-latent-depth","title":"Deep Transformers with Latent Depth","date":"2020-09-28","arxiv_id":"2009.13102","repositories_listed":1,"syntology":null},{"url":"/paper/kobe-knowledge-based-machine-translation","slug":"kobe-knowledge-based-machine-translation","title":"KoBE: Knowledge-Based Machine Translation Evaluation","date":"2020-09-23","arxiv_id":"2009.11027","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-from-limited","slug":"representation-learning-from-limited","title":"Representation Learning from Limited Educational Data with Crowdsourced Labels","date":"2020-09-23","arxiv_id":"2009.11222","repositories_listed":1,"syntology":null},{"url":"/paper/sdst-successive-decoding-for-speech-to-text","slug":"sdst-successive-decoding-for-speech-to-text","title":"Consecutive Decoding for Speech-to-text Translation","date":"2020-09-21","arxiv_id":"2009.09737","repositories_listed":1,"syntology":null},{"url":"/paper/energy-based-reranking-improving-neural","slug":"energy-based-reranking-improving-neural","title":"Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models","date":"2020-09-20","arxiv_id":"2009.13267","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/energy-based-reranking-improving-neural#ran","syntology_url":"https://syntology.ai/paper/2009.13267","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13267"}},"official":{"repos":["rooshenas/ebr_mt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/not-low-resource-anymore-aligner-ensembling","slug":"not-low-resource-anymore-aligner-ensembling","title":"Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation","date":"2020-09-20","arxiv_id":"2009.09359","repositories_listed":1,"syntology":null},{"url":"/paper/towards-computational-linguistics-in","slug":"towards-computational-linguistics-in","title":"Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation","date":"2020-09-19","arxiv_id":"2009.09309","repositories_listed":1,"syntology":null},{"url":"/paper/comet-a-neural-framework-for-mt-evaluation","slug":"comet-a-neural-framework-for-mt-evaluation","title":"COMET: A Neural Framework for MT Evaluation","date":"2020-09-18","arxiv_id":"2009.09025","repositories_listed":1,"syntology":null},{"url":"/paper/reusing-a-pretrained-language-model-on","slug":"reusing-a-pretrained-language-model-on","title":"Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT","date":"2020-09-16","arxiv_id":"2009.07610","repositories_listed":1,"syntology":null},{"url":"/paper/text-generation-by-learning-from-off-policy","slug":"text-generation-by-learning-from-off-policy","title":"Text Generation by Learning from Demonstrations","date":"2020-09-16","arxiv_id":"2009.07839","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":2,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 2 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) · 1 unverified","sample_list":"/paper/text-generation-by-learning-from-off-policy#ran","syntology_url":"https://syntology.ai/paper/2009.07839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.07839"}},"official":{"repos":["yzpang/gold-off-policy-text-gen-iclr21"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/a-study-of-genetic-algorithms-for","slug":"a-study-of-genetic-algorithms-for","title":"A Study of Genetic Algorithms for Hyperparameter Optimization of Neural Networks in Machine Translation","date":"2020-09-15","arxiv_id":"2009.08928","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-refinement-in-the-continuous-space","slug":"iterative-refinement-in-the-continuous-space","title":"Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation","date":"2020-09-15","arxiv_id":"2009.07177","repositories_listed":1,"syntology":null},{"url":"/paper/simultaneous-machine-translation-with-visual","slug":"simultaneous-machine-translation-with-visual","title":"Simultaneous Machine Translation with Visual Context","date":"2020-09-15","arxiv_id":"2009.07310","repositories_listed":1,"syntology":null},{"url":"/paper/why-not-simply-translate-a-first-swedish","slug":"why-not-simply-translate-a-first-swedish","title":"Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity","date":"2020-09-07","arxiv_id":"2009.03116","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-context-guided-capsule-network-for","slug":"dynamic-context-guided-capsule-network-for","title":"Dynamic Context-guided Capsule Network for Multimodal Machine Translation","date":"2020-09-04","arxiv_id":"2009.02016","repositories_listed":1,"syntology":null},{"url":"/paper/bidirectional-attention-network-for-monocular","slug":"bidirectional-attention-network-for-monocular","title":"Bidirectional Attention Network for Monocular Depth Estimation","date":"2020-09-01","arxiv_id":"2009.00743","repositories_listed":1,"syntology":null},{"url":"/paper/lite-training-strategies-for-portuguese","slug":"lite-training-strategies-for-portuguese","title":"Lite Training Strategies for Portuguese-English and English-Portuguese Translation","date":"2020-08-20","arxiv_id":"2008.08769","repositories_listed":1,"syntology":null},{"url":"/paper/but-fit-at-semeval-2020-task-4-multilingual","slug":"but-fit-at-semeval-2020-task-4-multilingual","title":"BUT-FIT at SemEval-2020 Task 4: Multilingual commonsense","date":"2020-08-17","arxiv_id":"2008.07259","repositories_listed":1,"syntology":null},{"url":"/paper/approaching-neural-chinese-word-segmentation","slug":"approaching-neural-chinese-word-segmentation","title":"Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task","date":"2020-08-12","arxiv_id":"2008.05348","repositories_listed":1,"syntology":null},{"url":"/paper/a-parallel-evaluation-data-set-of-software","slug":"a-parallel-evaluation-data-set-of-software","title":"A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation","date":"2020-08-11","arxiv_id":"2008.04550","repositories_listed":1,"syntology":null},{"url":"/paper/paraphrase-generation-as-zero-shot","slug":"paraphrase-generation-as-zero-shot","title":"Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity","date":"2020-08-11","arxiv_id":"2008.04935","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/paraphrase-generation-as-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2008.04935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.04935"}},"official":{"repos":["thompsonb/prism"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-sockeye-2-neural-machine-translation","slug":"the-sockeye-2-neural-machine-translation","title":"The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020","date":"2020-08-11","arxiv_id":"2008.04885","repositories_listed":1,"syntology":null},{"url":"/paper/a-multilingual-neural-machine-translation","slug":"a-multilingual-neural-machine-translation","title":"A Multilingual Neural Machine Translation Model for Biomedical Data","date":"2020-08-06","arxiv_id":"2008.02878","repositories_listed":1,"syntology":null},{"url":"/paper/designing-the-business-conversation-corpus-1","slug":"designing-the-business-conversation-corpus-1","title":"Designing the Business Conversation Corpus","date":"2020-08-05","arxiv_id":"2008.01940","repositories_listed":1,"syntology":null},{"url":"/paper/bsl-1k-scaling-up-co-articulated-sign","slug":"bsl-1k-scaling-up-co-articulated-sign","title":"BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues","date":"2020-07-23","arxiv_id":"2007.12131","repositories_listed":1,"syntology":null},{"url":"/paper/neural-machine-translation-with-error","slug":"neural-machine-translation-with-error","title":"Neural Machine Translation with Error Correction","date":"2020-07-21","arxiv_id":"2007.10681","repositories_listed":1,"syntology":null},{"url":"/paper/coconut-combining-context-aware-neural","slug":"coconut-combining-context-aware-neural","title":"CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair","date":"2020-07-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-graph-based-multi-modal-fusion-1","slug":"a-novel-graph-based-multi-modal-fusion-1","title":"A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation","date":"2020-07-17","arxiv_id":"2007.08742","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-novel-graph-based-multi-modal-fusion-1#ran","syntology_url":"https://syntology.ai/paper/2007.08742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08742"}},"official":{"repos":["middlekisser/GMNMT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/modeling-voting-for-system-combination-in","slug":"modeling-voting-for-system-combination-in","title":"Modeling Voting for System Combination in Machine Translation","date":"2020-07-14","arxiv_id":"2007.06943","repositories_listed":1,"syntology":{"n":19,"n_ran":9,"n_constructed":0,"n_ran_checked":0,"n_instrument":9,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"9 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; 9 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/modeling-voting-for-system-combination-in#ran","syntology_url":"https://syntology.ai/paper/2007.06943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.06943"}},"official":{"repos":["THUNLP-MT/Voting4SC"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/sequence-generation-with-mixed","slug":"sequence-generation-with-mixed","title":"Sequence Generation with Mixed Representations","date":"2020-07-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adascale-sgd-a-user-friendly-algorithm-for","slug":"adascale-sgd-a-user-friendly-algorithm-for","title":"AdaScale SGD: A User-Friendly Algorithm for Distributed Training","date":"2020-07-09","arxiv_id":"2007.05105","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/adascale-sgd-a-user-friendly-algorithm-for#ran","syntology_url":"https://syntology.ai/paper/2007.05105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05105"}},"official":null}},{"url":"/paper/bilingual-dictionary-based-neural-machine-1","slug":"bilingual-dictionary-based-neural-machine-1","title":"Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences","date":"2020-07-06","arxiv_id":"2007.02671","repositories_listed":1,"syntology":null},{"url":"/paper/el-departamento-de-nosotros-how-machine","slug":"el-departamento-de-nosotros-how-machine","title":"El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks","date":"2020-07-03","arxiv_id":"2007.01955","repositories_listed":1,"syntology":null},{"url":"/paper/a-retrieve-and-rewrite-initialization-method","slug":"a-retrieve-and-rewrite-initialization-method","title":"A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/character-mapping-and-ad-hoc-adaptation","slug":"character-mapping-and-ad-hoc-adaptation","title":"Character Mapping and Ad-hoc Adaptation: Edinburgh's IWSLT 2020 Open Domain Translation System","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/clireval-evaluating-machine-translation-as-a","slug":"clireval-evaluating-machine-translation-as-a","title":"CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/exploring-model-consensus-to-generate","slug":"exploring-model-consensus-to-generate","title":"Exploring Model Consensus to Generate Translation Paraphrases","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hausamt-v1-0-towards-english-hausa-neural-1","slug":"hausamt-v1-0-towards-english-hausa-neural-1","title":"HausaMT v1.0: Towards English--Hausa Neural Machine Translation","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-transformer-for-multimodal-machine","slug":"multimodal-transformer-for-multimodal-machine","title":"Multimodal Transformer for Multimodal Machine Translation","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/parallel-sentence-mining-by-constrained","slug":"parallel-sentence-mining-by-constrained","title":"Parallel Sentence Mining by Constrained Decoding","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/simultaneous-translation-and-paraphrase-for","slug":"simultaneous-translation-and-paraphrase-for","title":"Simultaneous Translation and Paraphrase for Language Education","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-em-approach-to-non-autoregressive","slug":"an-em-approach-to-non-autoregressive","title":"An EM Approach to Non-autoregressive Conditional Sequence Generation","date":"2020-06-29","arxiv_id":"2006.16378","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/an-em-approach-to-non-autoregressive#ran","syntology_url":"https://syntology.ai/paper/2006.16378","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.16378"}},"official":null}},{"url":"/paper/a-deep-reinforced-model-for-zero-shot-cross-1","slug":"a-deep-reinforced-model-for-zero-shot-cross-1","title":"A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards","date":"2020-06-27","arxiv_id":"2006.15454","repositories_listed":1,"syntology":null},{"url":"/paper/graph-optimal-transport-for-cross-domain","slug":"graph-optimal-transport-for-cross-domain","title":"Graph Optimal Transport for Cross-Domain Alignment","date":"2020-06-26","arxiv_id":"2006.14744","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":2,"n_no_contract":6,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 2 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-optimal-transport-for-cross-domain#ran","syntology_url":"https://syntology.ai/paper/2006.14744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.14744"}},"official":{"repos":["LiqunChen0606/Graph-Optimal-Transport"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/modeling-baroque-two-part-counterpoint-with","slug":"modeling-baroque-two-part-counterpoint-with","title":"Modeling Baroque Two-Part Counterpoint with Neural Machine Translation","date":"2020-06-25","arxiv_id":"2006.14221","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-quantum-neural-networks","slug":"recurrent-quantum-neural-networks","title":"Recurrent Quantum Neural Networks","date":"2020-06-25","arxiv_id":"2006.14619","repositories_listed":1,"syntology":null},{"url":"/paper/nlpcontributions-an-annotation-scheme-for","slug":"nlpcontributions-an-annotation-scheme-for","title":"NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature","date":"2020-06-23","arxiv_id":"2006.12870","repositories_listed":1,"syntology":null},{"url":"/paper/self-knowledge-distillation-a-simple-way-for","slug":"self-knowledge-distillation-a-simple-way-for","title":"Self-Knowledge Distillation with Progressive Refinement of Targets","date":"2020-06-22","arxiv_id":"2006.12000","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-knowledge-distillation-a-simple-way-for#ran","syntology_url":"https://syntology.ai/paper/2006.12000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12000"}},"official":{"repos":["lgcnsai/ps-kd-pytorch"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-learning-rates-with-maximum","slug":"adaptive-learning-rates-with-maximum","title":"MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients","date":"2020-06-21","arxiv_id":"2006.11918","repositories_listed":1,"syntology":null},{"url":"/paper/memory-transformer","slug":"memory-transformer","title":"Memory Transformer","date":"2020-06-20","arxiv_id":"2006.11527","repositories_listed":1,"syntology":null},{"url":"/paper/multi-branch-attentive-transformer","slug":"multi-branch-attentive-transformer","title":"Multi-branch Attentive Transformer","date":"2020-06-18","arxiv_id":"2006.10270","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-branch-attentive-transformer#ran","syntology_url":"https://syntology.ai/paper/2006.10270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.10270"}},"official":null}},{"url":"/paper/cross-lingual-retrieval-for-iterative-self","slug":"cross-lingual-retrieval-for-iterative-self","title":"Cross-lingual Retrieval for Iterative Self-Supervised Training","date":"2020-06-16","arxiv_id":"2006.09526","repositories_listed":1,"syntology":null},{"url":"/paper/epie-dataset-a-corpus-for-possible-idiomatic","slug":"epie-dataset-a-corpus-for-possible-idiomatic","title":"EPIE Dataset: A Corpus For Possible Idiomatic Expressions","date":"2020-06-16","arxiv_id":"2006.09479","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-computational-power-of-transformers","slug":"on-the-computational-power-of-transformers","title":"On the Computational Power of Transformers and its Implications in Sequence Modeling","date":"2020-06-16","arxiv_id":"2006.09286","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":12,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":11,"n_pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-the-computational-power-of-transformers#ran","syntology_url":"https://syntology.ai/paper/2006.09286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.09286"}},"official":{"repos":["satwik77/Transformer-Computation-Analysis"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dyne-dynamic-ensemble-decoding-for-multi","slug":"dyne-dynamic-ensemble-decoding-for-multi","title":"DynE: Dynamic Ensemble Decoding for Multi-Document Summarization","date":"2020-06-15","arxiv_id":"2006.08748","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dyne-dynamic-ensemble-decoding-for-multi#ran","syntology_url":"https://syntology.ai/paper/2006.08748","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08748"}},"official":{"repos":["chrishokamp/dynamic-transformer-ensembles"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fine-grained-human-evaluation-of-transformer","slug":"fine-grained-human-evaluation-of-transformer","title":"Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-Chinese","date":"2020-06-15","arxiv_id":"2006.08297","repositories_listed":1,"syntology":null},{"url":"/paper/ffr-v1-1-fon-french-neural-machine","slug":"ffr-v1-1-fon-french-neural-machine","title":"FFR v1.1: Fon-French Neural Machine Translation","date":"2020-06-14","arxiv_id":"2006.09217","repositories_listed":1,"syntology":null},{"url":"/paper/tangled-up-in-bleu-reevaluating-the","slug":"tangled-up-in-bleu-reevaluating-the","title":"Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics","date":"2020-06-11","arxiv_id":"2006.06264","repositories_listed":1,"syntology":null},{"url":"/paper/gender-in-danger-evaluating-speech","slug":"gender-in-danger-evaluating-speech","title":"Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus","date":"2020-06-10","arxiv_id":"2006.05754","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-recover-from-multi-modality","slug":"learning-to-recover-from-multi-modality","title":"Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation","date":"2020-06-09","arxiv_id":"2006.05165","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-to-recover-from-multi-modality#ran","syntology_url":"https://syntology.ai/paper/2006.05165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05165"}},"official":{"repos":["ranqiu92/RecoverSAT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/wat-zei-je-detecting-out-of-distribution","slug":"wat-zei-je-detecting-out-of-distribution","title":"Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers","date":"2020-06-08","arxiv_id":"2006.08344","repositories_listed":1,"syntology":null},{"url":"/paper/pre-training-polish-transformer-based","slug":"pre-training-polish-transformer-based","title":"Pre-training Polish Transformer-based Language Models at Scale","date":"2020-06-07","arxiv_id":"2006.04229","repositories_listed":1,"syntology":null},{"url":"/paper/m3p-learning-universal-representations-via","slug":"m3p-learning-universal-representations-via","title":"M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training","date":"2020-06-04","arxiv_id":"2006.02635","repositories_listed":1,"syntology":null},{"url":"/paper/mle-guided-parameter-search-for-task-loss","slug":"mle-guided-parameter-search-for-task-loss","title":"MLE-guided parameter search for task loss minimization in neural sequence modeling","date":"2020-06-04","arxiv_id":"2006.03158","repositories_listed":1,"syntology":null},{"url":"/paper/multi-agent-cross-translated-diversification","slug":"multi-agent-cross-translated-diversification","title":"Cross-model Back-translated Distillation for Unsupervised Machine Translation","date":"2020-06-03","arxiv_id":"2006.02163","repositories_listed":1,"syntology":null},{"url":"/paper/norm-based-curriculum-learning-for-neural","slug":"norm-based-curriculum-learning-for-neural","title":"Norm-Based Curriculum Learning for Neural Machine Translation","date":"2020-06-03","arxiv_id":"2006.02014","repositories_listed":1,"syntology":null},{"url":"/paper/building-a-swedish-question-answering-model","slug":"building-a-swedish-question-answering-model","title":"Building a Swedish Question-Answering Model","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cascaded-text-generation-with-markov","slug":"cascaded-text-generation-with-markov","title":"Cascaded Text Generation with Markov Transformers","date":"2020-06-01","arxiv_id":"2006.01112","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cascaded-text-generation-with-markov#ran","syntology_url":"https://syntology.ai/paper/2006.01112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.01112"}},"official":{"repos":["harvardnlp/cascaded-generation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/online-versus-offline-nmt-quality-an-in-depth","slug":"online-versus-offline-nmt-quality-an-in-depth","title":"Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English","date":"2020-06-01","arxiv_id":"2006.00814","repositories_listed":1,"syntology":null},{"url":"/paper/phone-features-improve-speech-translation","slug":"phone-features-improve-speech-translation","title":"Phone Features Improve Speech Translation","date":"2020-05-27","arxiv_id":"2005.13681","repositories_listed":1,"syntology":null},{"url":"/paper/the-unreasonable-volatility-of-neural-machine","slug":"the-unreasonable-volatility-of-neural-machine","title":"The Unreasonable Volatility of Neural Machine Translation Models","date":"2020-05-25","arxiv_id":"2005.12398","repositories_listed":1,"syntology":null},{"url":"/paper/multimwe-building-a-multi-lingual-multi-word","slug":"multimwe-building-a-multi-lingual-multi-word","title":"MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora","date":"2020-05-21","arxiv_id":"2005.10583","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-wait-k-models-for-simultaneous","slug":"efficient-wait-k-models-for-simultaneous","title":"Efficient Wait-k Models for Simultaneous Machine Translation","date":"2020-05-18","arxiv_id":"2005.08595","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-wait-k-models-for-simultaneous#ran","syntology_url":"https://syntology.ai/paper/2005.08595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08595"}},"official":{"repos":["elbayadm/attn2d"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/grammatical-gender-associations-outweigh","slug":"grammatical-gender-associations-outweigh","title":"Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings","date":"2020-05-18","arxiv_id":"2005.08864","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-fusion-of-attention-and-sequence-to","slug":"a-novel-fusion-of-attention-and-sequence-to","title":"A Novel Fusion of Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech","date":"2020-05-15","arxiv_id":"2005.08722","repositories_listed":1,"syntology":null},{"url":"/paper/reassessing-claims-of-human-parity-and-super","slug":"reassessing-claims-of-human-parity-and-super","title":"Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019","date":"2020-05-12","arxiv_id":"2005.05738","repositories_listed":1,"syntology":null},{"url":"/paper/does-multi-encoder-help-a-case-study-on","slug":"does-multi-encoder-help-a-case-study-on","title":"Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation","date":"2020-05-07","arxiv_id":"2005.03393","repositories_listed":1,"syntology":null},{"url":"/paper/jass-japanese-specific-sequence-to-sequence","slug":"jass-japanese-specific-sequence-to-sequence","title":"JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation","date":"2020-05-07","arxiv_id":"2005.03361","repositories_listed":1,"syntology":null},{"url":"/paper/on-exposure-bias-hallucination-and-domain","slug":"on-exposure-bias-hallucination-and-domain","title":"On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation","date":"2020-05-07","arxiv_id":"2005.03642","repositories_listed":1,"syntology":null},{"url":"/paper/it-s-easier-to-translate-out-of-english-than","slug":"it-s-easier-to-translate-out-of-english-than","title":"It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information","date":"2020-05-05","arxiv_id":"2005.02354","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/it-s-easier-to-translate-out-of-english-than#ran","syntology_url":"https://syntology.ai/paper/2005.02354","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.02354"}},"official":{"repos":["e-bug/nmt-difficulty"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-programming-encoding-for-subword","slug":"dynamic-programming-encoding-for-subword","title":"Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation","date":"2020-05-03","arxiv_id":"2005.06606","repositories_listed":1,"syntology":null},{"url":"/paper/how-does-selective-mechanism-improve-self","slug":"how-does-selective-mechanism-improve-self","title":"How Does Selective Mechanism Improve Self-Attention Networks?","date":"2020-05-03","arxiv_id":"2005.00979","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-inference-calibration-of-neural","slug":"on-the-inference-calibration-of-neural","title":"On the Inference Calibration of Neural Machine Translation","date":"2020-05-03","arxiv_id":"2005.00963","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/on-the-inference-calibration-of-neural#ran","syntology_url":"https://syntology.ai/paper/2005.00963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00963"}},"official":{"repos":["shuo-git/InfECE"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/on-the-limitations-of-cross-lingual-encoders","slug":"on-the-limitations-of-cross-lingual-encoders","title":"On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation","date":"2020-05-03","arxiv_id":"2005.01196","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/on-the-limitations-of-cross-lingual-encoders#ran","syntology_url":"https://syntology.ai/paper/2005.01196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.01196"}},"official":{"repos":["AIPHES/ACL20-Reference-Free-MT-Evaluation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/engine-energy-based-inference-networks-for","slug":"engine-energy-based-inference-networks-for","title":"ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation","date":"2020-05-02","arxiv_id":"2005.00850","repositories_listed":1,"syntology":null},{"url":"/paper/hard-coded-gaussian-attention-for-neural","slug":"hard-coded-gaussian-attention-for-neural","title":"Hard-Coded Gaussian Attention for Neural Machine Translation","date":"2020-05-02","arxiv_id":"2005.00742","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/hard-coded-gaussian-attention-for-neural#ran","syntology_url":"https://syntology.ai/paper/2005.00742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00742"}},"official":{"repos":["fallcat/stupidNMT"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/synthesizer-rethinking-self-attention-in","slug":"synthesizer-rethinking-self-attention-in","title":"Synthesizer: Rethinking Self-Attention in Transformer Models","date":"2020-05-02","arxiv_id":"2005.00743","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/synthesizer-rethinking-self-attention-in#ran","syntology_url":"https://syntology.ai/paper/2005.00743","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00743"}},"official":null}},{"url":"/paper/an-evaluation-benchmark-for-testing-the-word","slug":"an-evaluation-benchmark-for-testing-the-word","title":"An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-multidomain-english-indonesian","slug":"benchmarking-multidomain-english-indonesian","title":"Benchmarking Multidomain English-Indonesian Machine Translation","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/comparing-statistical-and-neural-models-for","slug":"comparing-statistical-and-neural-models-for","title":"Comparing Statistical and Neural Models for Learning Sound Correspondences","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/compositional-language-continual-learning","slug":"compositional-language-continual-learning","title":"Compositional Language Continual Learning","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/methodological-aspects-of-developing-and","slug":"methodological-aspects-of-developing-and","title":"Methodological Aspects of Developing and Managing an Etymological Lexical Resource: Introducing EtymDB-2.0","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/neural-machine-translation-with-universal","slug":"neural-machine-translation-with-universal","title":"Neural Machine Translation with Universal Visual Representation","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"843bf890a7f3f3bc9421d75f9bf61d47af1726bce95ac95abe75ebf2166e1216","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}