{"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/bilstm/papers/5","list_of":"/method/bilstm","method":"BiLSTM","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":5,"pages_in_order":7,"rows_per_page":100,"rows":[401,500],"of":657,"counts":{"archive_papers_tagged":657,"with_a_code_link":252,"where_syntology_ran_a_sample":43,"not_listed_spam_title":0,"listed":657,"listed_where_code_ran":43,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":35,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":35,"listed_every_run_a_failure_of_syntologys_instrument":8,"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/bilstm","prev":"/method/bilstm/papers/4","next":"/method/bilstm/papers/6","papers":[{"paper":"/paper/deepsentipers-novel-deep-learning-models","slug":"deepsentipers-novel-deep-learning-models","title":"DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus","date":"2020-04-11","arxiv_id":"2004.05328","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-disfluency-detection-by-self","title":"Improving Disfluency Detection by Self-Training a Self-Attentive Model","date":"2020-04-11","arxiv_id":"2004.05323","n_code_links":0,"syntology":null},{"paper":"/paper/dialbert-a-hierarchical-pre-trained-model-for","slug":"dialbert-a-hierarchical-pre-trained-model-for","title":"DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement","date":"2020-04-08","arxiv_id":"2004.03760","n_code_links":1,"syntology":null},{"paper":"/paper/scatter-selective-context-attentional-scene","slug":"scatter-selective-context-attentional-scene","title":"SCATTER: Selective Context Attentional Scene Text Recognizer","date":"2020-03-25","arxiv_id":"2003.11288","n_code_links":2,"syntology":null},{"paper":"/paper/semantic-based-end-to-end-learning-for","slug":"semantic-based-end-to-end-learning-for","title":"Semantic-based End-to-End Learning for Typhoon Intensity Prediction","date":"2020-03-22","arxiv_id":"2003.13779","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-on-contextual-embeddings","title":"A Survey on Contextual Embeddings","date":"2020-03-16","arxiv_id":"2003.07278","n_code_links":0,"syntology":null},{"paper":null,"slug":"parsing-early-modern-english-for-linguistic","title":"Parsing Early Modern English for Linguistic Search","date":"2020-02-24","arxiv_id":"2002.10546","n_code_links":0,"syntology":null},{"paper":"/paper/hhh-an-online-medical-chatbot-system-based-on-1","slug":"hhh-an-online-medical-chatbot-system-based-on-1","title":"HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention","date":"2020-02-08","arxiv_id":"2002.03140","n_code_links":2,"syntology":null},{"paper":null,"slug":"fully-hierarchical-fine-grained-prosody","title":"Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis","date":"2020-02-06","arxiv_id":"2002.03785","n_code_links":0,"syntology":null},{"paper":"/paper/wavetts-tacotron-based-tts-with-joint-time","slug":"wavetts-tacotron-based-tts-with-joint-time","title":"WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss","date":"2020-02-02","arxiv_id":"2002.00417","n_code_links":0,"syntology":null},{"paper":"/paper/aggressionnet-generalised-multi-modal-deep","slug":"aggressionnet-generalised-multi-modal-deep","title":"A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts","date":"2020-01-15","arxiv_id":"2001.05493","n_code_links":0,"syntology":null},{"paper":"/paper/tha3aroon-at-nsurl-2019-task-8-semantic","slug":"tha3aroon-at-nsurl-2019-task-8-semantic","title":"Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic","date":"2019-12-28","arxiv_id":"1912.12514","n_code_links":1,"syntology":null},{"paper":"/paper/probing-the-phonetic-and-phonological","slug":"probing-the-phonetic-and-phonological","title":"Probing the phonetic and phonological knowledge of tones in Mandarin TTS models","date":"2019-12-23","arxiv_id":"1912.10915","n_code_links":1,"syntology":null},{"paper":"/paper/pre-trained-contextual-embedding-of-source-1","slug":"pre-trained-contextual-embedding-of-source-1","title":"Learning and Evaluating Contextual Embedding of Source Code","date":"2019-12-21","arxiv_id":"2001.00059","n_code_links":2,"syntology":null},{"paper":null,"slug":"efficient-convolutional-neural-networks-for-3","title":"Efficient Convolutional Neural Networks for Diacritic Restoration","date":"2019-12-14","arxiv_id":"1912.06900","n_code_links":0,"syntology":null},{"paper":null,"slug":"long-length-legal-document-classification","title":"Long-length Legal Document Classification","date":"2019-12-14","arxiv_id":"1912.06905","n_code_links":0,"syntology":null},{"paper":"/paper/towards-robust-toxic-content-classification","slug":"towards-robust-toxic-content-classification","title":"Towards Robust Toxic Content Classification","date":"2019-12-14","arxiv_id":"1912.06872","n_code_links":1,"syntology":null},{"paper":null,"slug":"can-ai-generate-love-advice-toward-neural","title":"Can AI Generate Love Advice?: Toward Neural Answer Generation for Non-Factoid Questions","date":"2019-12-06","arxiv_id":"1912.10163","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-study-of-pretrained-language","title":"A Comparative Study of Pretrained Language Models on Thai Social Text Categorization","date":"2019-12-03","arxiv_id":"1912.01580","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-quality-elmo-embeddings-for-seven-less","title":"High Quality ELMo Embeddings for Seven Less-Resourced Languages","date":"2019-11-22","arxiv_id":"1911.10049","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-analysis-of-forecasting","title":"A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM","date":"2019-11-21","arxiv_id":"1911.09512","n_code_links":0,"syntology":null},{"paper":null,"slug":"question-generation-from-paragraphs-a-tale-of-1","title":"Question Generation from Paragraphs: A Tale of Two Hierarchical Models","date":"2019-11-08","arxiv_id":"1911.03407","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-contextualized-representation","slug":"hierarchical-contextualized-representation","title":"Hierarchical Contextualized Representation for Named Entity Recognition","date":"2019-11-06","arxiv_id":"1911.02257","n_code_links":1,"syntology":{"ran":9,"of":9,"n_ran_checked":8,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["cslydia/Hire-NER"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-deep-learning-approach-for-hindi-named","title":"A Deep Learning approach for Hindi Named Entity Recognition","date":"2019-11-05","arxiv_id":"1911.01421","n_code_links":0,"syntology":null},{"paper":null,"slug":"incremental-sense-weight-training-for-the","title":"Incremental Sense Weight Training for the Interpretation of Contextualized Word Embeddings","date":"2019-11-05","arxiv_id":"1911.01623","n_code_links":0,"syntology":null},{"paper":null,"slug":"graphene-a-precise-biomedical-literature","title":"GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment","date":"2019-11-02","arxiv_id":"1911.00760","n_code_links":0,"syntology":null},{"paper":null,"slug":"bme-uw-at-srst-2019-surface-realization-with","title":"BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"jbnu-at-mrp-2019-multi-level-biaffine","title":"JBNU at MRP 2019: Multi-level Biaffine Attention for Semantic Dependency Parsing","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"justdeep-at-nlp4if-2019-task-1-propaganda","title":"JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"named-entity-recognition-is-there-a-glass-1","title":"Named Entity Recognition - Is There a Glass Ceiling?","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nsitnlp4if-2019-propaganda-detection-from","title":"NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"on-sentence-representations-for-propaganda","title":"On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"reevaluating-argument-component-extraction-in","title":"Reevaluating Argument Component Extraction in Low Resource Settings","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-semantic-role-labeling-with","title":"Semi-Supervised Semantic Role Labeling with Cross-View Training","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sentence-level-propaganda-detection-in-news","title":"Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-feasibility-of-embedding-based-automatic","title":"The Feasibility of Embedding Based Automatic Evaluation for Single Document Summarization","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/tupa-at-mrp-2019-a-multi-task-baseline-system","slug":"tupa-at-mrp-2019-a-multi-task-baseline-system","title":"TUPA at MRP 2019: A Multi-Task Baseline System","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-labeled-parsing-with-deep-inside","title":"Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"uzhcraft-st-a-sequence-labeling-approach-to","title":"UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"what-does-this-word-mean-explaining","title":"What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition","date":"2019-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/191013291","slug":"191013291","title":"Sentence Embeddings for Russian NLU","date":"2019-10-29","arxiv_id":"1910.13291","n_code_links":1,"syntology":null},{"paper":"/paper/open-the-boxes-of-words-incorporating-sememes","slug":"open-the-boxes-of-words-incorporating-sememes","title":"Word-level Textual Adversarial Attacking as Combinatorial Optimization","date":"2019-10-27","arxiv_id":"1910.12196","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["thunlp/SememePSO-Attack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/mellotron-multispeaker-expressive-voice","slug":"mellotron-multispeaker-expressive-voice","title":"Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens","date":"2019-10-26","arxiv_id":"1910.11997","n_code_links":5,"syntology":null},{"paper":null,"slug":"evolution-of-transfer-learning-in-natural","title":"Evolution of transfer learning in natural language processing","date":"2019-10-16","arxiv_id":"1910.07370","n_code_links":0,"syntology":null},{"paper":"/paper/dual-path-rnn-efficient-long-sequence","slug":"dual-path-rnn-efficient-long-sequence","title":"Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation","date":"2019-10-14","arxiv_id":"1910.06379","n_code_links":8,"syntology":{"ran":19,"of":22,"n_ran_checked":17,"n_instrument":2,"unverified":3,"pointer_only":0,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 1 honoured, 1 violated, 15 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/sleeper-interpretable-sleep-staging-via","slug":"sleeper-interpretable-sleep-staging-via","title":"SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules","date":"2019-10-14","arxiv_id":"1910.06100","n_code_links":0,"syntology":null},{"paper":null,"slug":"why-attention-analyzing-and-remedying-bilstm","title":"Why Attention? Analyzing and Remedying BiLSTM Deficiency in Modeling Cross-Context for NER","date":"2019-10-07","arxiv_id":"1910.02586","n_code_links":0,"syntology":null},{"paper":"/paper/named-entity-recognition-is-there-a-glass","slug":"named-entity-recognition-is-there-a-glass","title":"Named Entity Recognition -- Is there a glass ceiling?","date":"2019-10-06","arxiv_id":"1910.02403","n_code_links":1,"syntology":null},{"paper":null,"slug":"data-efficient-goal-oriented-conversation","title":"Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks","date":"2019-10-03","arxiv_id":"1910.01302","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-multi-task-natural-language","slug":"hierarchical-multi-task-natural-language","title":"Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU","date":"2019-10-02","arxiv_id":"1910.00912","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"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) · 0 unverified","official":null}},{"paper":"/paper/specializing-word-embeddings-for-parsing-by","slug":"specializing-word-embeddings-for-parsing-by","title":"Specializing Word Embeddings (for Parsing) by Information Bottleneck","date":"2019-10-01","arxiv_id":"1910.00163","n_code_links":1,"syntology":null},{"paper":"/paper/fine-tune-bert-for-docred-with-two-step","slug":"fine-tune-bert-for-docred-with-two-step","title":"Fine-tune Bert for DocRED with Two-step Process","date":"2019-09-26","arxiv_id":"1909.11898","n_code_links":1,"syntology":null},{"paper":null,"slug":"extreme-language-model-compression-with-1","title":"Extremely Small BERT Models from Mixed-Vocabulary Training","date":"2019-09-25","arxiv_id":"1909.11687","n_code_links":0,"syntology":null},{"paper":"/paper/190910430","slug":"190910430","title":"Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings","date":"2019-09-23","arxiv_id":"1909.10430","n_code_links":1,"syntology":null},{"paper":"/paper/automatic-identification-and-normalisation-of","slug":"automatic-identification-and-normalisation-of","title":"Automatic Identification and Normalisation of Physical Measurements in Scientific Literature","date":"2019-09-23","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"subword-elmo","title":"Subword ELMo","date":"2019-09-18","arxiv_id":"1909.08357","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrofitting-contextualized-word-embeddings","title":"Retrofitting Contextualized Word Embeddings with Paraphrases","date":"2019-09-12","arxiv_id":"1909.09700","n_code_links":0,"syntology":null},{"paper":"/paper/uer-an-open-source-toolkit-for-pre-training","slug":"uer-an-open-source-toolkit-for-pre-training","title":"UER: An Open-Source Toolkit for Pre-training Models","date":"2019-09-12","arxiv_id":"1909.05658","n_code_links":2,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"1 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; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["dbiir/UER-py"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/from-english-to-code-switching-transfer","slug":"from-english-to-code-switching-transfer","title":"From English to Code-Switching: Transfer Learning with Strong Morphological Clues","date":"2019-09-11","arxiv_id":"1909.05158","n_code_links":1,"syntology":null},{"paper":"/paper/multimodal-embeddings-from-language-models","slug":"multimodal-embeddings-from-language-models","title":"Multimodal Embeddings from Language Models","date":"2019-09-10","arxiv_id":"1909.04302","n_code_links":1,"syntology":null},{"paper":"/paper/designing-and-interpreting-probes-with","slug":"designing-and-interpreting-probes-with","title":"Designing and Interpreting Probes with Control Tasks","date":"2019-09-08","arxiv_id":"1909.03368","n_code_links":1,"syntology":null},{"paper":null,"slug":"to-lemmatize-or-not-to-lemmatize-how-word","title":"To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation","date":"2019-09-06","arxiv_id":"1909.03135","n_code_links":0,"syntology":null},{"paper":"/paper/semantics-aware-bert-for-language","slug":"semantics-aware-bert-for-language","title":"Semantics-aware BERT for Language Understanding","date":"2019-09-05","arxiv_id":"1909.02209","n_code_links":1,"syntology":{"ran":7,"of":12,"n_ran_checked":5,"n_instrument":2,"unverified":5,"pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","official":{"repos":["cooelf/SemBERT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/aspect-detection-using-word-and-char","slug":"aspect-detection-using-word-and-char","title":"Aspect Detection using Word and Char Embeddings with (Bi)LSTM and CRF","date":"2019-09-03","arxiv_id":"1909.01276","n_code_links":1,"syntology":null},{"paper":null,"slug":"classification-betters-regression-in-query","title":"Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b","date":"2019-09-02","arxiv_id":"1909.00542","n_code_links":0,"syntology":null},{"paper":"/paper/how-contextual-are-contextualized-word","slug":"how-contextual-are-contextualized-word","title":"How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings","date":"2019-09-02","arxiv_id":"1909.00512","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluation-of-stacked-embeddings-for","title":"Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-vector-embedding-models-in","title":"Evaluation of vector embedding models in clustering of text documents","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multilingual-probing-of-deep-pre-trained","title":"Multilingual Probing of Deep Pre-Trained Contextual Encoders","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/neural-feature-extraction-for-contextual","slug":"neural-feature-extraction-for-contextual","title":"Neural Feature Extraction for Contextual Emotion Detection","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-sentiment-of-polish-language-short","title":"Predicting Sentiment of Polish Language Short Texts","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"quasi-bidirectional-encoder-representations","title":"Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"semantic-role-labeling-with-pretrained","title":"Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/sparse-victory-a-large-scale-systematic","slug":"sparse-victory-a-large-scale-systematic","title":"Sparse Victory -- A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification","date":"2019-09-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"speculation-and-negation-detection-in-french","title":"Speculation and Negation detection in French biomedical corpora","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-learning-with-contextual","title":"Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER","date":"2019-08-31","arxiv_id":"1909.00153","n_code_links":0,"syntology":null},{"paper":"/paper/evaluation-benchmarks-and-learning","slug":"evaluation-benchmarks-and-learning","title":"Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations","date":"2019-08-31","arxiv_id":"1909.00142","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ZeweiChu/DiscoEval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/remedying-bilstm-cnn-deficiency-in-modeling","slug":"remedying-bilstm-cnn-deficiency-in-modeling","title":"Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER","date":"2019-08-29","arxiv_id":"1908.11046","n_code_links":3,"syntology":null},{"paper":"/paper/scientific-statement-classification-over","slug":"scientific-statement-classification-over","title":"Scientific Statement Classification over arXiv.org","date":"2019-08-29","arxiv_id":"1908.10993","n_code_links":6,"syntology":null},{"paper":null,"slug":"shallow-syntax-in-deep-water","title":"Shallow Syntax in Deep Water","date":"2019-08-29","arxiv_id":"1908.11047","n_code_links":0,"syntology":null},{"paper":null,"slug":"onto-word-segmentation-of-the-complete-tang","title":"Onto Word Segmentation of the Complete Tang Poems","date":"2019-08-28","arxiv_id":"1908.10621","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-effectiveness-of-low-rank-matrix","title":"On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression","date":"2019-08-27","arxiv_id":"1908.09982","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-toxicity-in-news-articles","slug":"detecting-toxicity-in-news-articles","title":"Detecting Toxicity in News Articles: Application to Bulgarian","date":"2019-08-26","arxiv_id":"1908.09785","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-attribute-representation-and","slug":"rethinking-attribute-representation-and","title":"Rethinking Attribute Representation and Injection for Sentiment Classification","date":"2019-08-26","arxiv_id":"1908.09590","n_code_links":1,"syntology":null},{"paper":null,"slug":"adversarial-domain-adaptation-for-machine","title":"Adversarial Domain Adaptation for Machine Reading Comprehension","date":"2019-08-24","arxiv_id":"1908.09209","n_code_links":0,"syntology":null},{"paper":"/paper/bert-for-coreference-resolution-baselines-and","slug":"bert-for-coreference-resolution-baselines-and","title":"BERT for Coreference Resolution: Baselines and Analysis","date":"2019-08-24","arxiv_id":"1908.09091","n_code_links":2,"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/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/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/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/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":"semi-supervised-thai-sentence-segmentation","title":"Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations","date":"2019-08-04","arxiv_id":"1908.01294","n_code_links":0,"syntology":null},{"paper":null,"slug":"augmenting-named-entity-recognition-with","title":"Augmenting Named Entity Recognition with Commonsense Knowledge","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cbnu-system-for-sigmorphon-2019-shared-task-2","title":"CBNU System for SIGMORPHON 2019 Shared Task 2: a Pipeline Model","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cuni-malta-system-at-sigmorphon-2019-shared","title":"CUNI--Malta system at SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-adverse-drug-reaction-mentions","title":"Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo","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":null,"slug":"source-source-conditional-elmo-style-model","title":"SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-role-of-protected-class-word-lists-in","title":"The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/neural-mention-detection","slug":"neural-mention-detection","title":"Neural Mention Detection","date":"2019-07-29","arxiv_id":"1907.12524","n_code_links":1,"syntology":null}],"record_sha256":"db957707191729f1deb5133f25b234d3ea9ef1384157fbb5b4d47b5d6a49dc4a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}