{"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":"/method/adam/papers/236","list_of":"/method/adam","method":"Adam","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":236,"pages_in_order":244,"rows_per_page":100,"rows":[23501,23600],"of":24390,"counts":{"archive_papers_tagged":24390,"with_a_code_link":10944,"where_syntology_ran_a_sample":3424,"not_listed_spam_title":0,"listed":24390,"listed_where_code_ran":3424,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2899,"every_run_a_failure_of_syntologys_instrument":525,"listed_with_a_run_with_no_instrument_failure":2899,"listed_every_run_a_failure_of_syntologys_instrument":525,"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":"/method/adam","prev":"/method/adam/papers/235","next":"/method/adam/papers/237","papers":[{"paper":"/paper/text-summarization-with-pretrained-encoders","slug":"text-summarization-with-pretrained-encoders","title":"Text Summarization with Pretrained Encoders","date":"2019-08-22","arxiv_id":"1908.08345","n_code_links":19,"syntology":{"ran":13,"of":21,"n_ran_checked":12,"n_instrument":1,"unverified":8,"pointer_only":5,"phrase":"13 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; 1 where Syntology's instrument failed) · 8 unverified","official":{"repos":["nlpyang/PreSumm"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/the-compositionality-of-neural-networks","slug":"the-compositionality-of-neural-networks","title":"Compositionality decomposed: how do neural networks generalise?","date":"2019-08-22","arxiv_id":"1908.08351","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["i-machine-think/am-i-compositional"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/vl-bert-pre-training-of-generic-visual","slug":"vl-bert-pre-training-of-generic-visual","title":"VL-BERT: Pre-training of Generic Visual-Linguistic Representations","date":"2019-08-22","arxiv_id":"1908.08530","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"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","official":{"repos":["jackroos/VL-BERT"],"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"]}}},{"paper":null,"slug":"190807688","title":"Improving Neural Machine Translation with Pre-trained Representation","date":"2019-08-21","arxiv_id":"1908.07688","n_code_links":0,"syntology":null},{"paper":"/paper/190807721","slug":"190807721","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","date":"2019-08-21","arxiv_id":"1908.07721","n_code_links":1,"syntology":null},{"paper":null,"slug":"revealing-the-dark-secrets-of-bert","title":"Revealing the Dark Secrets of BERT","date":"2019-08-21","arxiv_id":"1908.08593","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-contextualized-embeddings-on-54","slug":"evaluating-contextualized-embeddings-on-54","title":"Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing","date":"2019-08-20","arxiv_id":"1908.07448","n_code_links":0,"syntology":null},{"paper":"/paper/glossbert-bert-for-word-sense-disambiguation","slug":"glossbert-bert-for-word-sense-disambiguation","title":"GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge","date":"2019-08-20","arxiv_id":"1908.07245","n_code_links":3,"syntology":null},{"paper":"/paper/lxmert-learning-cross-modality-encoder","slug":"lxmert-learning-cross-modality-encoder","title":"LXMERT: Learning Cross-Modality Encoder Representations from Transformers","date":"2019-08-20","arxiv_id":"1908.07490","n_code_links":9,"syntology":{"ran":4,"of":15,"n_ran_checked":4,"n_instrument":0,"unverified":11,"pointer_only":3,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 11 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","official":{"repos":["airsplay/lxmert"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/pix2pose-pixel-wise-coordinate-regression-of","slug":"pix2pose-pixel-wise-coordinate-regression-of","title":"Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation","date":"2019-08-20","arxiv_id":"1908.07433","n_code_links":3,"syntology":{"ran":4,"of":8,"n_ran_checked":4,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":"/paper/universal-adversarial-triggers-for-nlp","slug":"universal-adversarial-triggers-for-nlp","title":"Universal Adversarial Triggers for Attacking and Analyzing NLP","date":"2019-08-20","arxiv_id":"1908.07125","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"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) · 0 unverified","official":{"repos":["Eric-Wallace/universal-triggers"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-study-of-bert-for-non-factoid-question","title":"A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints","date":"2019-08-19","arxiv_id":"1908.06780","n_code_links":0,"syntology":null},{"paper":"/paper/align-mask-and-select-a-simple-method-for","slug":"align-mask-and-select-a-simple-method-for","title":"Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models","date":"2019-08-19","arxiv_id":"1908.06725","n_code_links":0,"syntology":null},{"paper":"/paper/encoder-agnostic-adaptation-for-conditional","slug":"encoder-agnostic-adaptation-for-conditional","title":"Encoder-Agnostic Adaptation for Conditional Language Generation","date":"2019-08-19","arxiv_id":"1908.06938","n_code_links":1,"syntology":null},{"paper":"/paper/neural-architectures-for-nested-ner-through-1","slug":"neural-architectures-for-nested-ner-through-1","title":"Neural Architectures for Nested NER through Linearization","date":"2019-08-19","arxiv_id":"1908.06926","n_code_links":1,"syntology":null},{"paper":"/paper/emotionx-idea-emotion-bert-an-affectional","slug":"emotionx-idea-emotion-bert-an-affectional","title":"EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation","date":"2019-08-17","arxiv_id":"1908.06264","n_code_links":1,"syntology":null},{"paper":null,"slug":"hard-but-robust-easy-but-sensitive-how","title":"Hard but Robust, Easy but Sensitive: How Encoder and Decoder Perform in Neural Machine Translation","date":"2019-08-17","arxiv_id":"1908.06259","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-features-matter-effective-language","title":"Language Features Matter: Effective Language Representations for Vision-Language Tasks","date":"2019-08-17","arxiv_id":"1908.06327","n_code_links":0,"syntology":null},{"paper":"/paper/bert-based-multi-head-selection-for-joint","slug":"bert-based-multi-head-selection-for-joint","title":"BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction","date":"2019-08-16","arxiv_id":"1908.05908","n_code_links":1,"syntology":null},{"paper":null,"slug":"cfo-a-framework-for-building-production-nlp","title":"CFO: A Framework for Building Production NLP Systems","date":"2019-08-16","arxiv_id":"1908.06121","n_code_links":0,"syntology":null},{"paper":"/paper/clutrr-a-diagnostic-benchmark-for-inductive","slug":"clutrr-a-diagnostic-benchmark-for-inductive","title":"CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text","date":"2019-08-16","arxiv_id":"1908.06177","n_code_links":5,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"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) · 0 unverified","official":{"repos":["facebookresearch/clutrr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"iterative-update-and-unified-representation","title":"Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning","date":"2019-08-16","arxiv_id":"1908.06758","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-transference-architecture-for-automatic","title":"The Transference Architecture for Automatic Post-Editing","date":"2019-08-16","arxiv_id":"1908.06151","n_code_links":0,"syntology":null},{"paper":"/paper/unicoder-vl-a-universal-encoder-for-vision","slug":"unicoder-vl-a-universal-encoder-for-vision","title":"Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training","date":"2019-08-16","arxiv_id":"1908.06066","n_code_links":0,"syntology":null},{"paper":"/paper/m-bert-injecting-multimodal-information-in","slug":"m-bert-injecting-multimodal-information-in","title":"Integrating Multimodal Information in Large Pretrained Transformers","date":"2019-08-15","arxiv_id":"1908.05787","n_code_links":1,"syntology":null},{"paper":"/paper/temporal-collaborative-ranking-via","slug":"temporal-collaborative-ranking-via","title":"Temporal Collaborative Ranking Via Personalized Transformer","date":"2019-08-15","arxiv_id":"1908.05435","n_code_links":3,"syntology":null},{"paper":"/paper/towards-making-the-most-of-bert-in-neural","slug":"towards-making-the-most-of-bert-in-neural","title":"Towards Making the Most of BERT in Neural Machine Translation","date":"2019-08-15","arxiv_id":"1908.05672","n_code_links":2,"syntology":null},{"paper":null,"slug":"transformer-based-automatic-post-editing-with","title":"Transformer-based Automatic Post-Editing with a Context-Aware Encoding Approach for Multi-Source Inputs","date":"2019-08-15","arxiv_id":"1908.05679","n_code_links":0,"syntology":null},{"paper":null,"slug":"visualizing-and-understanding-the","title":"Visualizing and Understanding the Effectiveness of BERT","date":"2019-08-15","arxiv_id":"1908.05620","n_code_links":0,"syntology":null},{"paper":null,"slug":"adabot-fault-tolerant-java-decompiler","title":"Adabot: Fault-Tolerant Java Decompiler","date":"2019-08-14","arxiv_id":"1908.06748","n_code_links":0,"syntology":null},{"paper":"/paper/establishing-strong-baselines-for-the-new","slug":"establishing-strong-baselines-for-the-new","title":"Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT","date":"2019-08-14","arxiv_id":"1908.04943","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-robustness-of-projection-neural","title":"On-Device Text Representations Robust To Misspellings via Projections","date":"2019-08-14","arxiv_id":"1908.05763","n_code_links":0,"syntology":null},{"paper":"/paper/scalable-attentive-sentence-pair-modeling-via","slug":"scalable-attentive-sentence-pair-modeling-via","title":"Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding","date":"2019-08-14","arxiv_id":"1908.05161","n_code_links":1,"syntology":null},{"paper":"/paper/sg-net-syntax-guided-machine-reading","slug":"sg-net-syntax-guided-machine-reading","title":"SG-Net: Syntax-Guided Machine Reading Comprehension","date":"2019-08-14","arxiv_id":"1908.05147","n_code_links":1,"syntology":null},{"paper":"/paper/bioflair-pretrained-pooled-contextualized","slug":"bioflair-pretrained-pooled-contextualized","title":"BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks","date":"2019-08-13","arxiv_id":"1908.05760","n_code_links":1,"syntology":null},{"paper":"/paper/domain-adaptive-training-bert-for-response","slug":"domain-adaptive-training-bert-for-response","title":"An Effective Domain Adaptive Post-Training Method for BERT in Response Selection","date":"2019-08-13","arxiv_id":"1908.04812","n_code_links":1,"syntology":null},{"paper":"/paper/generative-question-refinement-with-deep","slug":"generative-question-refinement-with-deep","title":"Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System","date":"2019-08-13","arxiv_id":"1908.05604","n_code_links":1,"syntology":null},{"paper":"/paper/on-the-convergence-of-adabound-and-its","slug":"on-the-convergence-of-adabound-and-its","title":"On the Convergence of AdaBound and its Connection to SGD","date":"2019-08-13","arxiv_id":"1908.04457","n_code_links":2,"syntology":null},{"paper":"/paper/structbert-incorporating-language-structures","slug":"structbert-incorporating-language-structures","title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding","date":"2019-08-13","arxiv_id":"1908.04577","n_code_links":0,"syntology":null},{"paper":null,"slug":"jointly-aligning-millions-of-images-with-deep","title":"Jointly Aligning Millions of Images with Deep Penalised Reconstruction Congealing","date":"2019-08-12","arxiv_id":"1908.04130","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-validity-of-self-attention-as","title":"On Identifiability in Transformers","date":"2019-08-12","arxiv_id":"1908.04211","n_code_links":0,"syntology":null},{"paper":"/paper/taper-time-aware-patient-ehr-representation","slug":"taper-time-aware-patient-ehr-representation","title":"TAPER: Time-Aware Patient EHR Representation","date":"2019-08-11","arxiv_id":"1908.03971","n_code_links":2,"syntology":{"ran":10,"of":12,"n_ran_checked":9,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["sajaddarabi/TAPER","sajaddarabi/TAPER-EHR"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/dense-transformer-networks-for-brain-electron","slug":"dense-transformer-networks-for-brain-electron","title":"Dense Transformer Networks for Brain Electron Microscopy Image Segmentation","date":"2019-08-10","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modality-latent-interaction-network-for","title":"Multi-modality Latent Interaction Network for Visual Question Answering","date":"2019-08-10","arxiv_id":"1908.04289","n_code_links":0,"syntology":null},{"paper":null,"slug":"bert-based-ranking-for-biomedical-entity","title":"BERT-based Ranking for Biomedical Entity Normalization","date":"2019-08-09","arxiv_id":"1908.03548","n_code_links":0,"syntology":null},{"paper":null,"slug":"uds-submission-for-the-wmt-19-automatic-post","title":"UdS Submission for the WMT 19 Automatic Post-Editing Task","date":"2019-08-09","arxiv_id":"1908.03402","n_code_links":0,"syntology":null},{"paper":"/paper/visualbert-a-simple-and-performant-baseline","slug":"visualbert-a-simple-and-performant-baseline","title":"VisualBERT: A Simple and Performant Baseline for Vision and Language","date":"2019-08-09","arxiv_id":"1908.03557","n_code_links":10,"syntology":{"ran":4,"of":9,"n_ran_checked":2,"n_instrument":2,"unverified":5,"pointer_only":6,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":null,"slug":"incremental-reinforcement-learning-a-new","title":"Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods","date":"2019-08-08","arxiv_id":"1908.02974","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-variance-of-the-adaptive-learning-rate","slug":"on-the-variance-of-the-adaptive-learning-rate","title":"On the Variance of the Adaptive Learning Rate and Beyond","date":"2019-08-08","arxiv_id":"1908.03265","n_code_links":21,"syntology":{"ran":12,"of":13,"n_ran_checked":11,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["LiyuanLucasLiu/RAdam"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"fast-and-accurate-capitalization-and","title":"Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging","date":"2019-08-07","arxiv_id":"1908.02404","n_code_links":0,"syntology":null},{"paper":null,"slug":"tinysearch-semantics-based-search-engine","title":"TinySearch -- Semantics based Search Engine using Bert Embeddings","date":"2019-08-07","arxiv_id":"1908.02451","n_code_links":0,"syntology":null},{"paper":"/paper/clustering-of-deep-contextualized","slug":"clustering-of-deep-contextualized","title":"Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts","date":"2019-08-06","arxiv_id":"1908.02286","n_code_links":1,"syntology":null},{"paper":"/paper/predicting-prosodic-prominence-from-text-with","slug":"predicting-prosodic-prominence-from-text-with","title":"Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations","date":"2019-08-06","arxiv_id":"1908.02262","n_code_links":1,"syntology":null},{"paper":"/paper/vilbert-pretraining-task-agnostic","slug":"vilbert-pretraining-task-agnostic","title":"ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks","date":"2019-08-06","arxiv_id":"1908.02265","n_code_links":11,"syntology":{"ran":10,"of":34,"n_ran_checked":8,"n_instrument":2,"unverified":24,"pointer_only":34,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 24 unverified","official":null}},{"paper":"/paper/beyond-english-only-reading-comprehension","slug":"beyond-english-only-reading-comprehension","title":"Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian","date":"2019-08-05","arxiv_id":"1908.01519","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-neural-net-augmentation-to-bert-for","title":"Exploring Neural Net Augmentation to BERT for Question Answering on SQUAD 2.0","date":"2019-08-04","arxiv_id":"1908.01767","n_code_links":0,"syntology":null},{"paper":null,"slug":"invariance-based-adversarial-attack-on-neural","title":"Exploring the Robustness of NMT Systems to Nonsensical Inputs","date":"2019-08-03","arxiv_id":"1908.01165","n_code_links":0,"syntology":null},{"paper":"/paper/universal-transforming-geometric-network","slug":"universal-transforming-geometric-network","title":"Universal Transforming Geometric Network","date":"2019-08-02","arxiv_id":"1908.00723","n_code_links":1,"syntology":null},{"paper":null,"slug":"approaching-smm4h-with-merged-models-and","title":"Approaching SMM4H with Merged Models and Multi-task Learning","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"baidu-neural-machine-translation-systems-for","title":"Baidu Neural Machine Translation Systems for WMT19","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"bert-masked-language-modeling-for-co","title":"BERT Masked Language Modeling for Co-reference Resolution","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"bsc-participation-in-the-wmt-translation-of","title":"BSC Participation in the WMT Translation of Biomedical Abstracts","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"bsnlp2019-shared-task-submission-multisource","title":"BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"combining-local-and-document-level-context","title":"Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/cross-lingual-lemmatization-and-morphology","slug":"cross-lingual-lemmatization-and-morphology","title":"Cross-Lingual Lemmatization and Morphology Tagging with Two-Stage Multilingual BERT Fine-Tuning","date":"2019-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"cuedwmt19ewclms-1","title":"CUED@WMT19:EWC\\&LMs","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cuni-submission-for-low-resource-languages-in","title":"CUNI Submission for Low-Resource Languages in WMT News 2019","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dfki-nmt-submission-to-the-wmt19-news","title":"DFKI-NMT Submission to the WMT19 News Translation Task","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dut-bim-at-mediqa-2019-utilizing-transformer","title":"DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dut-nlp-at-mediqa-2019-an-adversarial-multi","title":"DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"effort-aware-neural-automatic-post-editing","title":"Effort-Aware Neural Automatic Post-Editing","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"equalizing-gender-bias-in-neural-machine","title":"Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"erroneous-data-generation-for-grammatical","title":"Erroneous data generation for Grammatical Error Correction","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-conjunction-disambiguation-on","title":"Evaluating Conjunction Disambiguation on English-to-German and French-to-German WMT 2019 Translation Hypotheses","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/fill-the-gap-exploiting-bert-for-pronoun","slug":"fill-the-gap-exploiting-bert-for-pronoun","title":"Fill the GAP: Exploiting BERT for Pronoun Resolution","date":"2019-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"filtering-pseudo-references-by-paraphrasing","title":"Filtering Pseudo-References by Paraphrasing for Automatic Evaluation of Machine Translation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"gendered-ambiguous-pronoun-gap-shared-task-at","title":"Gendered Ambiguous Pronoun (GAP) Shared Task at the Gender Bias in NLP Workshop 2019","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/hulmona-the-universal-language-model-in","slug":"hulmona-the-universal-language-model-in","title":"hULMonA: The Universal Language Model in Arabic","date":"2019-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"iitp-mt-system-for-gujarati-english-news","title":"IITP-MT System for Gujarati-English News Translation Task at WMT 2019","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/improving-robustness-of-neural-machine","slug":"improving-robustness-of-neural-machine","title":"Improving Robustness of Neural Machine Translation with Multi-task Learning","date":"2019-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"incorporating-source-syntax-into-transformer","title":"Incorporating Source Syntax into Transformer-Based Neural Machine Translation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"jhu-2019-robustness-task-system-description","title":"JHU 2019 Robustness Task System Description","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kb-nlg-from-knowledge-base-to-natural","title":"KB-NLG: From Knowledge Base to Natural Language Generation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kfu-nlp-team-at-smm4h-2019-tasks-want-to","title":"KFU NLP Team at SMM4H 2019 Tasks: Want to Extract Adverse Drugs Reactions from Tweets? BERT to The Rescue","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kingsofts-neural-machine-translation-system","title":"Kingsoft's Neural Machine Translation System for WMT19","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ku_ai-at-mediqa-2019-domain-specific-pre","title":"KU\\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kyoto-university-participation-to-the-wmt","title":"Kyoto University Participation to the WMT 2019 News Shared Task","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"liums-contributions-to-the-wmt2019-news","title":"LIUM's Contributions to the WMT2019 News Translation Task: Data and Systems for German-French Language Pairs","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mipt-system-for-world-level-quality","title":"MIPT System for World-Level Quality Estimation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/msnet-a-bert-based-network-for-gendered-1","slug":"msnet-a-bert-based-network-for-gendered-1","title":"MSnet: A BERT-based Network for Gendered Pronoun Resolution","date":"2019-08-01","arxiv_id":"1908.00308","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-headed-architecture-based-on-bert-for","title":"Multi-headed Architecture Based on BERT for Grammatical Errors Correction","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ncuee-at-mediqa-2019-medical-text-inference","title":"NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/neural-grammatical-error-correction-systems","slug":"neural-grammatical-error-correction-systems","title":"Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data","date":"2019-08-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-machine-translation-of-low-resource","title":"Neural Machine Translation of Low-Resource and Similar Languages with Backtranslation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nicts-machine-translation-systems-for-the","title":"NICT's Machine Translation Systems for the WMT19 Similar Language Translation Task","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"no-army-no-navy-bert-semi-supervised-learning","title":"No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"noisy-channel-for-low-resource-grammatical","title":"Noisy Channel for Low Resource Grammatical Error Correction","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"on-gap-coreference-resolution-shared-task","title":"On GAP Coreference Resolution Shared Task: Insights from the 3rd Place Solution","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"panlp-at-mediqa-2019-pre-trained-language","title":"PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"qe-bert-bilingual-bert-using-multi-task","title":"QE BERT: Bilingual BERT Using Multi-task Learning for Neural Quality Estimation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"record_sha256":"51adf656ac51ed5515dc9524e46291d9e81fc11291919d9d9549cabf741589cb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}