{"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/37","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":37,"pages_in_order":108,"rows_per_page":100,"rows":[3601,3700],"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/36","next":"/task/sentence/papers/38","papers":[{"url":"/paper/neural-lattice-language-models","slug":"neural-lattice-language-models","title":"Neural Lattice Language Models","date":"2018-03-13","arxiv_id":"1803.05071","repositories_listed":1,"syntology":null},{"url":"/paper/concatenated-power-mean-word-embeddings-as","slug":"concatenated-power-mean-word-embeddings-as","title":"Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations","date":"2018-03-04","arxiv_id":"1803.01400","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/concatenated-power-mean-word-embeddings-as#ran","syntology_url":"https://syntology.ai/paper/1803.01400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.01400"}},"official":{"repos":["UKPLab/arxiv2018-xling-sentence-embeddings"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/analyzing-uncertainty-in-neural-machine","slug":"analyzing-uncertainty-in-neural-machine","title":"Analyzing Uncertainty in Neural Machine Translation","date":"2018-02-28","arxiv_id":"1803.00047","repositories_listed":1,"syntology":null},{"url":"/paper/simultaneously-self-attending-to-all-mentions","slug":"simultaneously-self-attending-to-all-mentions","title":"Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction","date":"2018-02-28","arxiv_id":"1802.10569","repositories_listed":1,"syntology":null},{"url":"/paper/ranking-sentences-for-extractive","slug":"ranking-sentences-for-extractive","title":"Ranking Sentences for Extractive Summarization with Reinforcement Learning","date":"2018-02-23","arxiv_id":"1802.08636","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/ranking-sentences-for-extractive#ran","syntology_url":"https://syntology.ai/paper/1802.08636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08636"}},"official":{"repos":["shashiongithub/Refresh"],"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/matching-long-text-documents-via-graph","slug":"matching-long-text-documents-via-graph","title":"Matching Article Pairs with Graphical Decomposition and Convolutions","date":"2018-02-21","arxiv_id":"1802.07459","repositories_listed":1,"syntology":null},{"url":"/paper/building-a-word-segmenter-for-sanskrit","slug":"building-a-word-segmenter-for-sanskrit","title":"Building a Word Segmenter for Sanskrit Overnight","date":"2018-02-17","arxiv_id":"1802.06185","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-compositionality-in-sentence","slug":"evaluating-compositionality-in-sentence","title":"Evaluating Compositionality in Sentence Embeddings","date":"2018-02-12","arxiv_id":"1802.04302","repositories_listed":1,"syntology":null},{"url":"/paper/augmenting-librispeech-with-french","slug":"augmenting-librispeech-with-french","title":"Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation","date":"2018-02-09","arxiv_id":"1802.03142","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-neural-network-based-semantic","slug":"recurrent-neural-network-based-semantic","title":"Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning","date":"2018-02-09","arxiv_id":"1802.03238","repositories_listed":1,"syntology":null},{"url":"/paper/quantitative-fine-grained-human-evaluation-of","slug":"quantitative-fine-grained-human-evaluation-of","title":"Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian","date":"2018-02-02","arxiv_id":"1802.01451","repositories_listed":1,"syntology":null},{"url":"/paper/reinforced-self-attention-network-a-hybrid-of","slug":"reinforced-self-attention-network-a-hybrid-of","title":"Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling","date":"2018-01-31","arxiv_id":"1801.10296","repositories_listed":1,"syntology":null},{"url":"/paper/a-question-focused-multi-factor-attention","slug":"a-question-focused-multi-factor-attention","title":"A Question-Focused Multi-Factor Attention Network for Question Answering","date":"2018-01-25","arxiv_id":"1801.08290","repositories_listed":1,"syntology":null},{"url":"/paper/sentipers-a-sentiment-analysis-corpus-for","slug":"sentipers-a-sentiment-analysis-corpus-for","title":"SentiPers: A Sentiment Analysis Corpus for Persian","date":"2018-01-23","arxiv_id":"1801.07737","repositories_listed":1,"syntology":null},{"url":"/paper/building-an-ellipsis-aware-chinese-dependency","slug":"building-an-ellipsis-aware-chinese-dependency","title":"Building an Ellipsis-aware Chinese Dependency Treebank for Web Text","date":"2018-01-20","arxiv_id":"1801.06613","repositories_listed":1,"syntology":null},{"url":"/paper/image-captioning-using-deep-neural","slug":"image-captioning-using-deep-neural","title":"Image Captioning using Deep Neural Architectures","date":"2018-01-17","arxiv_id":"1801.05568","repositories_listed":1,"syntology":null},{"url":"/paper/translating-pro-drop-languages-with","slug":"translating-pro-drop-languages-with","title":"Translating Pro-Drop Languages with Reconstruction Models","date":"2018-01-10","arxiv_id":"1801.03257","repositories_listed":1,"syntology":null},{"url":"/paper/mizan-a-large-persian-english-parallel-corpus","slug":"mizan-a-large-persian-english-parallel-corpus","title":"MIZAN: A Large Persian-English Parallel Corpus","date":"2018-01-07","arxiv_id":"1801.02107","repositories_listed":1,"syntology":null},{"url":"/paper/visual-text-correction","slug":"visual-text-correction","title":"Visual Text Correction","date":"2018-01-06","arxiv_id":"1801.01967","repositories_listed":1,"syntology":null},{"url":"/paper/basic-concepts-and-tools-for-the-toki-pona","slug":"basic-concepts-and-tools-for-the-toki-pona","title":"Basic concepts and tools for the Toki Pona minimal and constructed language: description of the language and main issues; analysis of the vocabulary; text synthesis and syntax highlighting; Wordnet synsets","date":"2017-12-26","arxiv_id":"1712.09359","repositories_listed":1,"syntology":null},{"url":"/paper/train-once-test-anywhere-zero-shot-learning","slug":"train-once-test-anywhere-zero-shot-learning","title":"Train Once, Test Anywhere: Zero-Shot Learning for Text Classification","date":"2017-12-16","arxiv_id":"1712.05972","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-text-generation-and-planning-for","slug":"hierarchical-text-generation-and-planning-for","title":"Hierarchical Text Generation and Planning for Strategic Dialogue","date":"2017-12-15","arxiv_id":"1712.05846","repositories_listed":1,"syntology":null},{"url":"/paper/relation-extraction-a-survey","slug":"relation-extraction-a-survey","title":"Relation Extraction : A Survey","date":"2017-12-14","arxiv_id":"1712.05191","repositories_listed":1,"syntology":null},{"url":"/paper/effective-neural-solution-for-multi-criteria","slug":"effective-neural-solution-for-multi-criteria","title":"Effective Neural Solution for Multi-Criteria Word Segmentation","date":"2017-12-07","arxiv_id":"1712.02856","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-oracle-making-use-of-ambiguity-in","slug":"hybrid-oracle-making-use-of-ambiguity-in","title":"Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese Dependency Parsing","date":"2017-11-28","arxiv_id":"1711.10163","repositories_listed":1,"syntology":null},{"url":"/paper/self-view-grounding-given-a-narrated-360","slug":"self-view-grounding-given-a-narrated-360","title":"Self-view Grounding Given a Narrated 360° Video","date":"2017-11-23","arxiv_id":"1711.08664","repositories_listed":1,"syntology":null},{"url":"/paper/crowdsourcing-question-answer-meaning","slug":"crowdsourcing-question-answer-meaning","title":"Crowdsourcing Question-Answer Meaning Representations","date":"2017-11-16","arxiv_id":"1711.05885","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/crowdsourcing-question-answer-meaning#ran","syntology_url":"https://syntology.ai/paper/1711.05885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.05885"}},"official":{"repos":["uwnlp/qamr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/automatic-extraction-of-commonsense","slug":"automatic-extraction-of-commonsense","title":"Automatic Extraction of Commonsense LocatedNear Knowledge","date":"2017-11-11","arxiv_id":"1711.04204","repositories_listed":1,"syntology":null},{"url":"/paper/towards-the-use-of-deep-reinforcement","slug":"towards-the-use-of-deep-reinforcement","title":"Towards the Use of Deep Reinforcement Learning with Global Policy For Query-based Extractive Summarisation","date":"2017-11-10","arxiv_id":"1711.03859","repositories_listed":1,"syntology":null},{"url":"/paper/improving-low-resource-neural-machine","slug":"improving-low-resource-neural-machine","title":"Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel Corpus","date":"2017-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-how-to-simplify-from-explicit","slug":"learning-how-to-simplify-from-explicit","title":"Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs","date":"2017-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/understanding-hidden-memories-of-recurrent","slug":"understanding-hidden-memories-of-recurrent","title":"Understanding Hidden Memories of Recurrent Neural Networks","date":"2017-10-30","arxiv_id":"1710.10777","repositories_listed":1,"syntology":null},{"url":"/paper/text-coherence-analysis-based-on-deep-neural","slug":"text-coherence-analysis-based-on-deep-neural","title":"Text Coherence Analysis Based on Deep Neural Network","date":"2017-10-21","arxiv_id":"1710.07770","repositories_listed":1,"syntology":null},{"url":"/paper/text2action-generative-adversarial-synthesis","slug":"text2action-generative-adversarial-synthesis","title":"Text2Action: Generative Adversarial Synthesis from Language to Action","date":"2017-10-15","arxiv_id":"1710.05298","repositories_listed":1,"syntology":null},{"url":"/paper/measurement-context-extraction-from-text","slug":"measurement-context-extraction-from-text","title":"Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science","date":"2017-10-11","arxiv_id":"1710.04312","repositories_listed":1,"syntology":null},{"url":"/paper/attentive-convolution-equipping-cnns-with-rnn","slug":"attentive-convolution-equipping-cnns-with-rnn","title":"Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms","date":"2017-10-02","arxiv_id":"1710.00519","repositories_listed":1,"syntology":null},{"url":"/paper/graph-convolutional-networks-for-named-entity-1","slug":"graph-convolutional-networks-for-named-entity-1","title":"Graph Convolutional Networks for Named Entity Recognition","date":"2017-09-28","arxiv_id":"1709.10053","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-classification-of-patient-notes-a","slug":"multi-label-classification-of-patient-notes-a","title":"Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment","date":"2017-09-27","arxiv_id":"1709.09587","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-explain-non-standard-english","slug":"learning-to-explain-non-standard-english","title":"Learning to Explain Non-Standard English Words and Phrases","date":"2017-09-26","arxiv_id":"1709.09254","repositories_listed":1,"syntology":null},{"url":"/paper/the-consciousness-prior","slug":"the-consciousness-prior","title":"The Consciousness Prior","date":"2017-09-25","arxiv_id":"1709.08568","repositories_listed":1,"syntology":null},{"url":"/paper/challenging-neural-dialogue-models-with","slug":"challenging-neural-dialogue-models-with","title":"Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena","date":"2017-09-22","arxiv_id":"1709.07840","repositories_listed":1,"syntology":null},{"url":"/paper/sequence-to-sequence-learning-for-event","slug":"sequence-to-sequence-learning-for-event","title":"Sequence to Sequence Learning for Event Prediction","date":"2017-09-18","arxiv_id":"1709.06033","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-generative-framework-for-paraphrase","slug":"a-deep-generative-framework-for-paraphrase","title":"A Deep Generative Framework for Paraphrase Generation","date":"2017-09-15","arxiv_id":"1709.05074","repositories_listed":1,"syntology":null},{"url":"/paper/stack-captioning-coarse-to-fine-learning-for","slug":"stack-captioning-coarse-to-fine-learning-for","title":"Stack-Captioning: Coarse-to-Fine Learning for Image Captioning","date":"2017-09-11","arxiv_id":"1709.03376","repositories_listed":1,"syntology":null},{"url":"/paper/globally-normalized-reader","slug":"globally-normalized-reader","title":"Globally Normalized Reader","date":"2017-09-08","arxiv_id":"1709.02828","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-visual-features-from-text-for","slug":"predicting-visual-features-from-text-for","title":"Predicting Visual Features from Text for Image and Video Caption Retrieval","date":"2017-09-05","arxiv_id":"1709.01362","repositories_listed":1,"syntology":null},{"url":"/paper/do-latent-tree-learning-models-identify","slug":"do-latent-tree-learning-models-identify","title":"Do latent tree learning models identify meaningful structure in sentences?","date":"2017-09-04","arxiv_id":"1709.01121","repositories_listed":1,"syntology":null},{"url":"/paper/learning-neural-word-salience-scores","slug":"learning-neural-word-salience-scores","title":"Learning Neural Word Salience Scores","date":"2017-09-04","arxiv_id":"1709.01186","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-representations-for-knowledge","slug":"context-aware-representations-for-knowledge","title":"Context-Aware Representations for Knowledge Base Relation Extraction","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/does-syntax-help-discourse-segmentation-not","slug":"does-syntax-help-discourse-segmentation-not","title":"Does syntax help discourse segmentation? Not so much","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/instance-weighting-for-neural-machine","slug":"instance-weighting-for-neural-machine","title":"Instance Weighting for Neural Machine Translation Domain Adaptation","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/same-same-but-different-compositionality-of","slug":"same-same-but-different-compositionality-of","title":"Same same, but different: Compositionality of paraphrase granularity levels","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/where-is-misty-interpreting-spatial","slug":"where-is-misty-interpreting-spatial","title":"Where is Misty? Interpreting Spatial Descriptors by Modeling Regions in Space","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/emoatt-at-emoint-2017-inner-attention","slug":"emoatt-at-emoint-2017-inner-attention","title":"EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity","date":"2017-08-18","arxiv_id":"1708.05521","repositories_listed":1,"syntology":null},{"url":"/paper/modality-specific-cross-modal-similarity","slug":"modality-specific-cross-modal-similarity","title":"Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network","date":"2017-08-16","arxiv_id":"1708.04776","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-speech-from-lip-dynamics","slug":"estimating-speech-from-lip-dynamics","title":"Estimating speech from lip dynamics","date":"2017-08-03","arxiv_id":"1708.01198","repositories_listed":1,"syntology":null},{"url":"/paper/learning-what-is-essential-in-questions","slug":"learning-what-is-essential-in-questions","title":"Learning What is Essential in Questions","date":"2017-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/udl-at-semeval-2017-task-1-semantic-textual","slug":"udl-at-semeval-2017-task-1-semantic-textual","title":"UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features","date":"2017-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adapting-sequence-models-for-sentence","slug":"adapting-sequence-models-for-sentence","title":"Adapting Sequence Models for Sentence Correction","date":"2017-07-27","arxiv_id":"1707.09067","repositories_listed":1,"syntology":null},{"url":"/paper/determining-semantic-textual-similarity-using","slug":"determining-semantic-textual-similarity-using","title":"Determining Semantic Textual Similarity using Natural Deduction Proofs","date":"2017-07-27","arxiv_id":"1707.08713","repositories_listed":1,"syntology":null},{"url":"/paper/strawman-an-ensemble-of-deep-bag-of-ngrams","slug":"strawman-an-ensemble-of-deep-bag-of-ngrams","title":"Strawman: an Ensemble of Deep Bag-of-Ngrams for Sentiment Analysis","date":"2017-07-26","arxiv_id":"1707.08939","repositories_listed":1,"syntology":null},{"url":"/paper/character-level-intra-attention-network-for","slug":"character-level-intra-attention-network-for","title":"Character-level Intra Attention Network for Natural Language Inference","date":"2017-07-24","arxiv_id":"1707.07469","repositories_listed":1,"syntology":null},{"url":"/paper/transition-based-generation-from-abstract","slug":"transition-based-generation-from-abstract","title":"Transition-Based Generation from Abstract Meaning Representations","date":"2017-07-24","arxiv_id":"1707.07591","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-rank-question-answer-pairs-with","slug":"learning-to-rank-question-answer-pairs-with","title":"Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture","date":"2017-07-20","arxiv_id":"1707.06372","repositories_listed":1,"syntology":null},{"url":"/paper/neural-reranking-for-named-entity-recognition","slug":"neural-reranking-for-named-entity-recognition","title":"Neural Reranking for Named Entity Recognition","date":"2017-07-17","arxiv_id":"1707.05127","repositories_listed":1,"syntology":null},{"url":"/paper/refining-raw-sentence-representations-for","slug":"refining-raw-sentence-representations-for","title":"Refining Raw Sentence Representations for Textual Entailment Recognition via Attention","date":"2017-07-11","arxiv_id":"1707.03103","repositories_listed":1,"syntology":null},{"url":"/paper/a-two-stage-parsing-method-for-text-level","slug":"a-two-stage-parsing-method-for-text-level","title":"A Two-Stage Parsing Method for Text-Level Discourse Analysis","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/abstract-meaning-representation-parsing-using","slug":"abstract-meaning-representation-parsing-using","title":"Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/error-repair-dependency-parsing-for","slug":"error-repair-dependency-parsing-for","title":"Error-repair Dependency Parsing for Ungrammatical Texts","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-neural-architecture-for-sentence-level","slug":"a-deep-neural-architecture-for-sentence-level","title":"A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking","date":"2017-06-25","arxiv_id":"1706.08032","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-syntactic-abilities-of-rnns","slug":"exploring-the-syntactic-abilities-of-rnns","title":"Exploring the Syntactic Abilities of RNNs with Multi-task Learning","date":"2017-06-12","arxiv_id":"1706.03542","repositories_listed":1,"syntology":null},{"url":"/paper/a-mention-ranking-model-for-abstract-anaphora","slug":"a-mention-ranking-model-for-abstract-anaphora","title":"A Mention-Ranking Model for Abstract Anaphora Resolution","date":"2017-06-07","arxiv_id":"1706.02256","repositories_listed":1,"syntology":null},{"url":"/paper/event-representations-for-automated-story","slug":"event-representations-for-automated-story","title":"Event Representations for Automated Story Generation with Deep Neural Nets","date":"2017-06-05","arxiv_id":"1706.01331","repositories_listed":1,"syntology":null},{"url":"/paper/crnn-a-joint-neural-network-for-redundancy","slug":"crnn-a-joint-neural-network-for-redundancy","title":"CRNN: A Joint Neural Network for Redundancy Detection","date":"2017-06-04","arxiv_id":"1706.01069","repositories_listed":1,"syntology":null},{"url":"/paper/listen-interact-and-talk-learning-to-speak","slug":"listen-interact-and-talk-learning-to-speak","title":"Listen, Interact and Talk: Learning to Speak via Interaction","date":"2017-05-28","arxiv_id":"1705.09906","repositories_listed":1,"syntology":null},{"url":"/paper/biomedical-event-trigger-identification-using","slug":"biomedical-event-trigger-identification-using","title":"Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models","date":"2017-05-26","arxiv_id":"1705.09516","repositories_listed":1,"syntology":null},{"url":"/paper/how-a-general-purpose-commonsense-ontology","slug":"how-a-general-purpose-commonsense-ontology","title":"How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval","date":"2017-05-24","arxiv_id":"1705.08844","repositories_listed":1,"syntology":null},{"url":"/paper/arc-swift-a-novel-transition-system-for","slug":"arc-swift-a-novel-transition-system-for","title":"Arc-swift: A Novel Transition System for Dependency Parsing","date":"2017-05-12","arxiv_id":"1705.04434","repositories_listed":1,"syntology":null},{"url":"/paper/generating-memorable-mnemonic-encodings-of","slug":"generating-memorable-mnemonic-encodings-of","title":"Generating Memorable Mnemonic Encodings of Numbers","date":"2017-05-07","arxiv_id":"1705.02700","repositories_listed":1,"syntology":null},{"url":"/paper/learning-distributed-representations-of-texts","slug":"learning-distributed-representations-of-texts","title":"Learning Distributed Representations of Texts and Entities from Knowledge Base","date":"2017-05-06","arxiv_id":"1705.02494","repositories_listed":1,"syntology":null},{"url":"/paper/joint-rnn-model-for-argument-component","slug":"joint-rnn-model-for-argument-component","title":"Joint RNN Model for Argument Component Boundary Detection","date":"2017-05-05","arxiv_id":"1705.02131","repositories_listed":1,"syntology":null},{"url":"/paper/chunk-based-bi-scale-decoder-for-neural","slug":"chunk-based-bi-scale-decoder-for-neural","title":"Chunk-Based Bi-Scale Decoder for Neural Machine Translation","date":"2017-05-03","arxiv_id":"1705.01452","repositories_listed":1,"syntology":null},{"url":"/paper/show-adapt-and-tell-adversarial-training-of","slug":"show-adapt-and-tell-adversarial-training-of","title":"Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner","date":"2017-05-02","arxiv_id":"1705.00930","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-for-low-resource-neural","slug":"data-augmentation-for-low-resource-neural","title":"Data Augmentation for Low-Resource Neural Machine Translation","date":"2017-05-01","arxiv_id":"1705.00440","repositories_listed":1,"syntology":null},{"url":"/paper/topically-driven-neural-language-model","slug":"topically-driven-neural-language-model","title":"Topically Driven Neural Language Model","date":"2017-04-26","arxiv_id":"1704.08012","repositories_listed":1,"syntology":null},{"url":"/paper/parsing-speech-a-neural-approach-to","slug":"parsing-speech-a-neural-approach-to","title":"Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information","date":"2017-04-24","arxiv_id":"1704.07287","repositories_listed":1,"syntology":null},{"url":"/paper/a-ccg-parsing-with-a-supertag-and-dependency","slug":"a-ccg-parsing-with-a-supertag-and-dependency","title":"A* CCG Parsing with a Supertag and Dependency Factored Model","date":"2017-04-23","arxiv_id":"1704.06936","repositories_listed":1,"syntology":null},{"url":"/paper/video-fill-in-the-blank-using-lrrl-lstms-with","slug":"video-fill-in-the-blank-using-lrrl-lstms-with","title":"Video Fill In the Blank using LR/RL LSTMs with Spatial-Temporal Attentions","date":"2017-04-15","arxiv_id":"1704.04689","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-cross-sentence-context-for-neural","slug":"exploiting-cross-sentence-context-for-neural","title":"Exploiting Cross-Sentence Context for Neural Machine Translation","date":"2017-04-14","arxiv_id":"1704.04347","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-and-cross-domain-discourse-1","slug":"cross-lingual-and-cross-domain-discourse-1","title":"Cross-lingual and cross-domain discourse segmentation of entire documents","date":"2017-04-13","arxiv_id":"1704.04100","repositories_listed":1,"syntology":null},{"url":"/paper/learning-joint-multilingual-sentence","slug":"learning-joint-multilingual-sentence","title":"Learning Joint Multilingual Sentence Representations with Neural Machine Translation","date":"2017-04-13","arxiv_id":"1704.04154","repositories_listed":1,"syntology":null},{"url":"/paper/learning-two-branch-neural-networks-for-image","slug":"learning-two-branch-neural-networks-for-image","title":"Learning Two-Branch Neural Networks for Image-Text Matching Tasks","date":"2017-04-11","arxiv_id":"1704.03470","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-information-retrieval-for","slug":"discriminative-information-retrieval-for","title":"Discriminative Information Retrieval for Question Answering Sentence Selection","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-analysis-of-citations-using","slug":"sentiment-analysis-of-citations-using","title":"Sentiment Analysis of Citations Using Word2vec","date":"2017-04-01","arxiv_id":"1704.00177","repositories_listed":1,"syntology":null},{"url":"/paper/using-coreference-links-to-improve-spanish-to","slug":"using-coreference-links-to-improve-spanish-to","title":"Using Coreference Links to Improve Spanish-to-English Machine Translation","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/which-is-the-effective-way-for-gaokao","slug":"which-is-the-effective-way-for-gaokao","title":"Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sentence-simplification-with-deep","slug":"sentence-simplification-with-deep","title":"Sentence Simplification with Deep Reinforcement Learning","date":"2017-03-31","arxiv_id":"1703.10931","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-argumentative-zoning-using-word2vec","slug":"automatic-argumentative-zoning-using-word2vec","title":"Automatic Argumentative-Zoning Using Word2vec","date":"2017-03-29","arxiv_id":"1703.10152","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-rnn-with-static-sentence-level","slug":"hierarchical-rnn-with-static-sentence-level","title":"Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection","date":"2017-03-22","arxiv_id":"1703.07713","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-generate-one-sentence-biographies","slug":"learning-to-generate-one-sentence-biographies","title":"Learning to generate one-sentence biographies from Wikidata","date":"2017-02-21","arxiv_id":"1702.06235","repositories_listed":1,"syntology":null}],"record_sha256":"c6cbd09bfbd28dd24a541bcc3ce887f43d0abf205b93961f4791d73cb2b683f2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}