{"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/sentence/papers/88","list_of":"/task/sentence","task":"Sentence","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":88,"pages_in_order":108,"rows_per_page":100,"rows":[8701,8800],"of":10752,"counts":{"archive_papers_tagged":10752,"with_a_code_link":3811,"where_syntology_ran_a_sample":657,"not_listed_spam_title":0,"listed":10752,"listed_where_code_ran":657,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":544,"every_run_a_failure_of_syntologys_instrument":113,"listed_with_a_run_with_no_instrument_failure":544,"listed_every_run_a_failure_of_syntologys_instrument":113,"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/sentence","prev":"/task/sentence/papers/87","next":"/task/sentence/papers/89","papers":[{"url":null,"slug":"is-word-segmentation-necessary-for-deep","title":"Is Word Segmentation Necessary for Deep Learning of Chinese Representations?","date":"2019-05-14","arxiv_id":"1905.05526","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-content-transfer-through-grounded","title":"Towards Content Transfer through Grounded Text Generation","date":"2019-05-13","arxiv_id":"1905.05293","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-inference-of-minimalist-grammars","title":"Automatic Inference of Minimalist Grammars using an SMT-Solver","date":"2019-05-08","arxiv_id":"1905.02869","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-parallel-corpus-of-full-text-1","title":"A Large Parallel Corpus of Full-Text Scientific Articles","date":"2019-05-06","arxiv_id":"1905.01852","repositories_listed":0,"syntology":null},{"url":"/paper/a-parallel-corpus-of-theses-and-dissertations","slug":"a-parallel-corpus-of-theses-and-dissertations","title":"A Parallel Corpus of Theses and Dissertations Abstracts","date":"2019-05-05","arxiv_id":"1905.01715","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-typedriven-vector-semantics-for-ellipsis","title":"A Typedriven Vector Semantics for Ellipsis with Anaphora using Lambek Calculus with Limited Contraction","date":"2019-05-05","arxiv_id":"1905.01647","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualization-of-morphological-inflection","title":"Contextualization of Morphological Inflection","date":"2019-05-04","arxiv_id":"1905.01420","repositories_listed":0,"syntology":null},{"url":null,"slug":"argument-identification-in-public-comments","title":"Argument Identification in Public Comments from eRulemaking","date":"2019-05-02","arxiv_id":"1905.00572","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowbias-a-novel-ai-method-to-detect-polarity","title":"KnowBias: A Novel AI Method to Detect Polarity in Online Content","date":"2019-05-02","arxiv_id":"1905.00724","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dynamic-semantics-for-causal","title":"A Dynamic Semantics for Causal Counterfactuals","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-non-linear-theory-for-sentence-embedding","title":"A NON-LINEAR THEORY FOR SENTENCE EMBEDDING","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/dense-temporal-convolution-network-for-sign","slug":"dense-temporal-convolution-network-for-sign","title":"Dense Temporal Convolution Network for Sign Language Translation","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"diverse-machine-translation-with-a-single","title":"Diverse Machine Translation with a Single Multinomial Latent Variable","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-composition-of-sentence-embeddings","title":"Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-sentence-representations-with-multi-1","title":"Improving Sentence Representations with Multi-view Frameworks","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"looking-for-elmos-friends-sentence-level-1","title":"Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-with-weakly-paired","title":"Machine Translation With Weakly Paired Bilingual Documents","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"minimum-divergence-vs-maximum-margin-an","title":"Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-for-semantic-parsing-with","title":"Multi-Task Learning for Semantic Parsing with Cross-Domain Sketch","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nlprolog-reasoning-with-weak-unification-for","title":"NLProlog: Reasoning with Weak Unification for Natural Language Question Answering","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-attention-based-deep-learning-method","title":"A self-attention based deep learning method for lesion attribute detection from CT reports","date":"2019-04-30","arxiv_id":"1904.13018","repositories_listed":0,"syntology":null},{"url":null,"slug":"logician-a-unified-end-to-end-neural-approach","title":"Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction","date":"2019-04-29","arxiv_id":"1904.12535","repositories_listed":0,"syntology":null},{"url":null,"slug":"190501996","title":"Neural Machine Translation with Recurrent Highway Networks","date":"2019-04-28","arxiv_id":"1905.01996","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-dataset-design-choices-for","title":"Understanding Dataset Design Choices for Multi-hop Reasoning","date":"2019-04-27","arxiv_id":"1904.12106","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointing-novel-objects-in-image-captioning","title":"Pointing Novel Objects in Image Captioning","date":"2019-04-25","arxiv_id":"1904.11251","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-what-different-nlp-tasks-teach","title":"Probing What Different NLP Tasks Teach Machines about Function Word Comprehension","date":"2019-04-25","arxiv_id":"1904.11544","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-tolerance-of-neural-machine","title":"Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors","date":"2019-04-24","arxiv_id":"1904.10997","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-machine-translated-paragraphs-by","title":"Detecting Machine-Translated Paragraphs by Matching Similar Words","date":"2019-04-24","arxiv_id":"1904.10641","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-token-level-explanations-for","title":"Generating Token-Level Explanations for Natural Language Inference","date":"2019-04-24","arxiv_id":"1904.10717","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-spoken-language-translation","title":"End-to-End Spoken Language Translation","date":"2019-04-23","arxiv_id":"1904.10760","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-unsupervised-pretraining-and","title":"Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge","date":"2019-04-22","arxiv_id":"1904.09705","repositories_listed":0,"syntology":null},{"url":null,"slug":"3g-structure-for-image-caption-generation","title":"3G structure for image caption generation","date":"2019-04-21","arxiv_id":"1904.09544","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-gated-recurrent-units-for-image","title":"Multi-modal gated recurrent units for image description","date":"2019-04-20","arxiv_id":"1904.09421","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-sentence-generation-using","title":"Personalized sentence generation using generative adversarial networks with author-specific word usage","date":"2019-04-20","arxiv_id":"1904.09442","repositories_listed":0,"syntology":null},{"url":null,"slug":"saliency-guided-attention-network-for-image","title":"Saliency-Guided Attention Network for Image-Sentence Matching","date":"2019-04-20","arxiv_id":"1904.09471","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-concept-based-adversarial","title":"Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings","date":"2019-04-20","arxiv_id":"1904.09446","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-focused-sentence-compression-in-linear","title":"Query-focused Sentence Compression in Linear Time","date":"2019-04-19","arxiv_id":"1904.09051","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-underlying-gender-bias-in","title":"Evaluating the Underlying Gender Bias in Contextualized Word Embeddings","date":"2019-04-18","arxiv_id":"1904.08783","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-collocate-neural-modules-for","title":"Learning to Collocate Neural Modules for Image Captioning","date":"2019-04-18","arxiv_id":"1904.08608","repositories_listed":0,"syntology":null},{"url":null,"slug":"patent-analytics-based-on-feature-vector","title":"Patent Analytics Based on Feature Vector Space Model: A Case of IoT","date":"2019-04-17","arxiv_id":"1904.08100","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-evaluation-of-text","title":"An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression","date":"2019-04-16","arxiv_id":"1904.08770","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-human-text-comprehension-through","title":"Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation","date":"2019-04-15","arxiv_id":"1904.07142","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-head-multi-layer-attention-to-deep","title":"Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection","date":"2019-04-15","arxiv_id":"1904.07334","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-cross-lingual-transfer-of-neural","title":"Scalable Cross-Lingual Transfer of Neural Sentence Embeddings","date":"2019-04-11","arxiv_id":"1904.05542","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-animations-from-screenplays","title":"Generating Animations from Screenplays","date":"2019-04-10","arxiv_id":"1904.05440","repositories_listed":0,"syntology":null},{"url":null,"slug":"nlprsrpol-at-semeval-2019-task-6-and-task-5","title":"NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier","date":"2019-04-10","arxiv_id":"1904.05152","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-gan-based-end-to-end-tts-training","title":"A New GAN-based End-to-End TTS Training Algorithm","date":"2019-04-09","arxiv_id":"1904.04775","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-selection-with-cluster-based-language","title":"Data Selection with Cluster-Based Language Difference Models and Cynical Selection","date":"2019-04-09","arxiv_id":"1904.04900","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-syntactic-features-in-a-parsed","title":"Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS","date":"2019-04-09","arxiv_id":"1904.04764","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-few-shot-learning-for-dual","title":"Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction","date":"2019-04-08","arxiv_id":"1904.03898","repositories_listed":0,"syntology":null},{"url":null,"slug":"licd-a-language-independent-approach-for","title":"LICD: A Language-Independent Approach for Aspect Category Detection","date":"2019-04-07","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"thisiscompetition-at-semeval-2019-task-9-bert","title":"ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples","date":"2019-04-06","arxiv_id":"1904.03339","repositories_listed":0,"syntology":null},{"url":null,"slug":"distinguishing-clinical-sentiment-the","title":"Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records","date":"2019-04-05","arxiv_id":"1904.03225","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-context-and-fragment-feature-usage","title":"Effective Context and Fragment Feature Usage for Named Entity Recognition","date":"2019-04-05","arxiv_id":"1904.03305","repositories_listed":0,"syntology":null},{"url":null,"slug":"outlier-detection-for-improved-data-quality","title":"Outlier Detection for Improved Data Quality and Diversity in Dialog Systems","date":"2019-04-05","arxiv_id":"1904.03122","repositories_listed":0,"syntology":null},{"url":"/paper/pomo-generating-entity-specific-post","slug":"pomo-generating-entity-specific-post","title":"PoMo: Generating Entity-Specific Post-Modifiers in Context","date":"2019-04-05","arxiv_id":"1904.03111","repositories_listed":0,"syntology":null},{"url":null,"slug":"composition-of-sentence-embeddingslessons","title":"Composition of Sentence Embeddings:Lessons from Statistical Relational Learning","date":"2019-04-04","arxiv_id":"1904.02464","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-level-n-ary-relation-extraction-with","title":"Document-Level $N$-ary Relation Extraction with Multiscale Representation Learning","date":"2019-04-04","arxiv_id":"1904.02347","repositories_listed":0,"syntology":null},{"url":null,"slug":"extract-and-edit-an-alternative-to-back","title":"Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation","date":"2019-04-04","arxiv_id":"1904.02331","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effect-of-downstream-classification-tasks","title":"The Effect of Downstream Classification Tasks for Evaluating Sentence Embeddings","date":"2019-04-03","arxiv_id":"1904.02228","repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-deep-structured-semantic-models","slug":"unsupervised-deep-structured-semantic-models","title":"Unsupervised Deep Structured Semantic Models for Commonsense Reasoning","date":"2019-04-03","arxiv_id":"1904.01938","repositories_listed":0,"syntology":null},{"url":null,"slug":"190412580","title":"Twitter Sentiment Analysis using Distributed Word and Sentence Representation","date":"2019-04-01","arxiv_id":"1904.12580","repositories_listed":0,"syntology":null},{"url":null,"slug":"emerald-110k-a-multidisciplinary-dataset-for","title":"Emerald 110k: A Multidisciplinary Dataset for Abstract Sentence Classification","date":"2019-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-considering-context","title":"Machine translation considering context information using Encoder-Decoder model","date":"2019-03-30","arxiv_id":"1904.00160","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-attention-generative-adversarial","title":"Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification","date":"2019-03-27","arxiv_id":"1903.11334","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-model-for-structured-information","title":"A model for structured information representation in neural networks","date":"2019-03-26","arxiv_id":"1611.03698","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-based-text-style","title":"Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus","date":"2019-03-26","arxiv_id":"1903.10671","repositories_listed":0,"syntology":null},{"url":null,"slug":"unpaired-image-captioning-via-scene-graph","title":"Unpaired Image Captioning via Scene Graph Alignments","date":"2019-03-26","arxiv_id":"1903.10658","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-extraction-between-the-clinical","title":"Relation extraction between the clinical entities based on the shortest dependency path based LSTM","date":"2019-03-24","arxiv_id":"1903.09941","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multi-level-information-for-dialogue","title":"Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer","date":"2019-03-21","arxiv_id":"1903.08953","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-check-worthiness-ranking-with-weak","title":"Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking","date":"2019-03-20","arxiv_id":"1903.08404","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-effective-label-noise-model-for-dnn-text","title":"An Effective Label Noise Model for DNN Text Classification","date":"2019-03-18","arxiv_id":"1903.07507","repositories_listed":0,"syntology":null},{"url":null,"slug":"formality-style-transfer-with-hybrid-textual","title":"Formality Style Transfer with Hybrid Textual Annotations","date":"2019-03-15","arxiv_id":"1903.06353","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-world-networks-for-summarization-of","title":"Small-world networks for summarization of biomedical articles","date":"2019-03-07","arxiv_id":"1903.02861","repositories_listed":0,"syntology":null},{"url":"/paper/a-synchronized-multi-modal-attention-caption","slug":"a-synchronized-multi-modal-attention-caption","title":"Human Attention in Image Captioning: Dataset and Analysis","date":"2019-03-06","arxiv_id":"1903.02499","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-lesion-annotation-in-ct-images","title":"Fine-grained lesion annotation in CT images with knowledge mined from radiology reports","date":"2019-03-04","arxiv_id":"1903.01505","repositories_listed":0,"syntology":null},{"url":"/paper/dream-a-challenge-data-set-and-models-for","slug":"dream-a-challenge-data-set-and-models-for","title":"DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension","date":"2019-03-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-contextual-representation-learning-1","title":"Efficient Contextual Representation Learning With Continuous Outputs","date":"2019-03-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"open-information-extraction-from-question","title":"Open Information Extraction from Question-Answer Pairs","date":"2019-03-01","arxiv_id":"1903.00172","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-s-my-head-definition-data-set-and","title":"Where's My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution","date":"2019-03-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-parametric-adaptation-for-neural-machine","title":"Non-Parametric Adaptation for Neural Machine Translation","date":"2019-02-28","arxiv_id":"1903.00058","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-based-curriculum","title":"Reinforcement Learning based Curriculum Optimization for Neural Machine Translation","date":"2019-02-28","arxiv_id":"1903.00041","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-decoding-event-related","title":"A Framework for Decoding Event-Related Potentials from Text","date":"2019-02-27","arxiv_id":"1902.10296","repositories_listed":0,"syntology":null},{"url":"/paper/an-editorial-network-for-enhanced-document","slug":"an-editorial-network-for-enhanced-document","title":"An Editorial Network for Enhanced Document Summarization","date":"2019-02-27","arxiv_id":"1902.10360","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-tree-lstm-structure-aware","title":"Interpretable Structure-aware Document Encoders with Hierarchical Attention","date":"2019-02-26","arxiv_id":"1902.09713","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-extraction-using-explicit-context","title":"Relation Extraction using Explicit Context Conditioning","date":"2019-02-25","arxiv_id":"1902.09271","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-sequences-via-learning","title":"Transfer Learning for Sequences via Learning to Collocate","date":"2019-02-25","arxiv_id":"1902.09092","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sentiment-analysis-using-a-graph-based","title":"Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications","date":"2019-02-23","arxiv_id":"1902.10247","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-multilingual-sentence-embedding","title":"Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax","date":"2019-02-22","arxiv_id":"1902.08564","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-autoregressive-machine-translation-with","title":"Non-Autoregressive Machine Translation with Auxiliary Regularization","date":"2019-02-22","arxiv_id":"1902.10245","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-in-speech-recognition-contextual","title":"Learned In Speech Recognition: Contextual Acoustic Word Embeddings","date":"2019-02-18","arxiv_id":"1902.06833","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-robot-speech-recognition-using","title":"Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization","date":"2019-02-13","arxiv_id":"1902.05446","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-compression-via-dc-programming","title":"Sentence Compression via DC Programming Approach","date":"2019-02-13","arxiv_id":"1902.07248","repositories_listed":0,"syntology":null},{"url":null,"slug":"pac-bayes-analysis-of-sentence-representation","title":"PAC-Bayes Analysis of Sentence Representation","date":"2019-02-12","arxiv_id":"1902.04247","repositories_listed":0,"syntology":null},{"url":null,"slug":"ls-tree-model-interpretation-when-the-data","title":"LS-Tree: Model Interpretation When the Data Are Linguistic","date":"2019-02-11","arxiv_id":"1902.04187","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-specific-opinion-expression-extraction","title":"Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network","date":"2019-02-07","arxiv_id":"1902.02709","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-single-attention-based-combination-of-cnn","title":"A Single Attention-Based Combination of CNN and RNN for Relation Classification","date":"2019-02-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ensemble-dialogue-system-for-facts-based","title":"An Ensemble Dialogue System for Facts-Based Sentence Generation","date":"2019-02-05","arxiv_id":"1902.01529","repositories_listed":0,"syntology":null},{"url":null,"slug":"realistic-image-generation-using-region","title":"Realistic Image Generation using Region-phrase Attention","date":"2019-02-04","arxiv_id":"1902.05395","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-oriented-text-summarization-based-on","title":"Query-oriented text summarization based on hypergraph transversals","date":"2019-02-02","arxiv_id":"1902.00672","repositories_listed":0,"syntology":null}],"record_sha256":"dbc6961229040ae85df6c19bb0854b1b2947e4fb31fcb60b672733eed351c762","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}