{"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/84","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":84,"pages_in_order":108,"rows_per_page":100,"rows":[8301,8400],"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/83","next":"/task/sentence/papers/85","papers":[{"url":null,"slug":"asgen-answer-containing-sentence-generation","title":"ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-over-phrases","title":"Attention over Phrases","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-improves-latent-space-geometry-in","title":"Denoising Improves Latent Space Geometry in Text Autoencoders","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eins-long-short-term-memory-with-extrapolated","title":"EINS: Long Short-Term Memory with Extrapolated Input Network Simplification","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-multi-sentence-abstractive-1","title":"Generating Multi-Sentence Abstractive Summaries of Interleaved Texts","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-network-structure-for-modeling","title":"Interpretable Network Structure for Modeling Contextual Dependency","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lattice-representation-learning","title":"Lattice Representation Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-entailment-based-sentence-embeddings","title":"Learning Entailment-Based Sentence Embeddings from Natural Language Inference","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-detect-opinion-snippet-for-aspect","title":"Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis","date":"2019-09-25","arxiv_id":"1909.11297","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-entanglement-entropy-for-deep","title":"Leveraging Entanglement Entropy for Deep Understanding of Attention Matrix in Text Matching","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lex-gan-layered-explainable-rumor-detector","title":"LEX-GAN: Layered Explainable Rumor Detector Based on Generative Adversarial Networks","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lossless-data-compression-with-transformer","title":"Lossless Data Compression with Transformer","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"proactive-sequence-generator-via-knowledge","title":"Proactive Sequence Generator via Knowledge Acquisition","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"putting-machine-translation-in-context-with","title":"Putting Machine Translation in Context with the Noisy Channel Model","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-hierarchical-topic-guided-neural","title":"Recurrent Hierarchical Topic-Guided Neural Language Models","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"regularly-varying-representation-for-sentence","title":"Regularly varying representation for sentence embedding","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-natural-language-representation","title":"Robust Natural Language Representation Learning for Natural Language Inference by Projecting Superficial Words out","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-text-style-transfer-cross","title":"Semi-supervised Text Style Transfer: Cross Projection in Latent Space","date":"2019-09-25","arxiv_id":"1909.11493","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-embedding-with-contrastive-multi","title":"Sentence embedding with contrastive multi-views learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-communities-a-text","title":"The Power of Communities: A Text Classification Model with Automated Labeling Process Using Network Community Detection","date":"2019-09-25","arxiv_id":"1909.11706","repositories_listed":0,"syntology":null},{"url":null,"slug":"wman-weakly-supervised-moment-alignment","title":"wMAN: WEAKLY-SUPERVISED MOMENT ALIGNMENT NETWORK FOR TEXT-BASED VIDEO SEGMENT RETRIEVAL","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"xd-cross-lingual-knowledge-distillation-for","title":"XD: Cross-lingual Knowledge Distillation for Polyglot Sentence Embeddings","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"situating-sentence-embedders-with-nearest","title":"Situating Sentence Embedders with Nearest Neighbor Overlap","date":"2019-09-24","arxiv_id":"1909.10724","repositories_listed":0,"syntology":null},{"url":null,"slug":"190910368","title":"A Consolidated System for Robust Multi-Document Entity Risk Extraction and Taxonomy Augmentation","date":"2019-09-23","arxiv_id":"1909.10368","repositories_listed":0,"syntology":null},{"url":null,"slug":"190910393","title":"Specificity-Based Sentence Ordering for Multi-Document Extractive Risk Summarization","date":"2019-09-23","arxiv_id":"1909.10393","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowbias-detecting-political-polarity-in-long","title":"KnowBias: Detecting Political Polarity in Long Text Content","date":"2019-09-22","arxiv_id":"1909.12230","repositories_listed":0,"syntology":null},{"url":null,"slug":"190909788","title":"Visuallly Grounded Generation of Entailments from Premises","date":"2019-09-21","arxiv_id":"1909.09788","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-create-sentence-semantic-relation","title":"Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization","date":"2019-09-20","arxiv_id":"1909.12231","repositories_listed":0,"syntology":null},{"url":"/paper/summary-level-training-of-sentence-rewriting","slug":"summary-level-training-of-sentence-rewriting","title":"Summary Level Training of Sentence Rewriting for Abstractive Summarization","date":"2019-09-19","arxiv_id":"1909.08752","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-we-need-neural-models-to-explain-human","title":"Do We Need Neural Models to Explain Human Judgments of Acceptability?","date":"2019-09-18","arxiv_id":"1909.08663","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-generation-for-non-expert","title":"Natural Language Generation for Non-Expert Users","date":"2019-09-18","arxiv_id":"1909.08250","repositories_listed":0,"syntology":null},{"url":null,"slug":"controllable-length-control-neural-encoder","title":"Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning","date":"2019-09-17","arxiv_id":"1909.09492","repositories_listed":0,"syntology":null},{"url":null,"slug":"bottlesum-unsupervised-and-self-supervised","title":"BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle","date":"2019-09-16","arxiv_id":"1909.07405","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-text-classification-using-unsupervised","title":"Short-Text Classification Using Unsupervised Keyword Expansion","date":"2019-09-16","arxiv_id":"1909.07512","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncovering-flaming-events-on-news-media-in","title":"Uncovering Flaming Events on News Media in Social Media","date":"2019-09-16","arxiv_id":"1909.07181","repositories_listed":0,"syntology":null},{"url":null,"slug":"induction-and-reference-of-entities-in-a","title":"Induction and Reference of Entities in a Visual Story","date":"2019-09-15","arxiv_id":"1909.09699","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architectures-for-fine-grained","title":"Neural Architectures for Fine-Grained Propaganda Detection in News","date":"2019-09-13","arxiv_id":"1909.06162","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-the-information-encoded-in-x-vectors","title":"Probing the Information Encoded in X-vectors","date":"2019-09-13","arxiv_id":"1909.06351","repositories_listed":0,"syntology":null},{"url":null,"slug":"sanvis-visual-analytics-for-understanding","title":"SANVis: Visual Analytics for Understanding Self-Attention Networks","date":"2019-09-13","arxiv_id":"1909.09595","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-to-sequence-pre-training-with-data","title":"Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting","date":"2019-09-13","arxiv_id":"1909.06002","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognition-of-handwritten-digit-using","title":"Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers","date":"2019-09-12","arxiv_id":"1909.08490","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrofitting-contextualized-word-embeddings","title":"Retrofitting Contextualized Word Embeddings with Paraphrases","date":"2019-09-12","arxiv_id":"1909.09700","repositories_listed":0,"syntology":null},{"url":null,"slug":"speculative-beam-search-for-simultaneous","title":"Speculative Beam Search for Simultaneous Translation","date":"2019-09-12","arxiv_id":"1909.05421","repositories_listed":0,"syntology":null},{"url":null,"slug":"dependency-aware-named-entity-recognition","title":"Dependency-Aware Named Entity Recognition with Relative and Global Attentions","date":"2019-09-11","arxiv_id":"1909.05166","repositories_listed":0,"syntology":null},{"url":null,"slug":"getting-gender-right-in-neural-machine-1","title":"Getting Gender Right in Neural Machine Translation","date":"2019-09-11","arxiv_id":"1909.05088","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-attention-branch-network-for","title":"Multimodal Attention Branch Network for Perspective-Free Sentence Generation","date":"2019-09-10","arxiv_id":"1909.05664","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-longer-the-better-the-interplay-between","title":"The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews","date":"2019-09-10","arxiv_id":"1909.05192","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-reward-learning-for-policy-gradient","title":"Transfer Reward Learning for Policy Gradient-Based Text Generation","date":"2019-09-09","arxiv_id":"1909.03622","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-paraphrasing-by-simulated","title":"Unsupervised Paraphrasing by Simulated Annealing","date":"2019-09-09","arxiv_id":"1909.03588","repositories_listed":0,"syntology":null},{"url":null,"slug":"countering-the-effects-of-lead-bias-in-news","title":"Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses","date":"2019-09-08","arxiv_id":"1909.04028","repositories_listed":0,"syntology":null},{"url":null,"slug":"may-i-check-again-a-simple-but-efficient-way","title":"May I Check Again? -- A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to French Legal Texts","date":"2019-09-08","arxiv_id":"1909.03453","repositories_listed":0,"syntology":null},{"url":null,"slug":"mule-multimodal-universal-language-embedding","title":"MULE: Multimodal Universal Language Embedding","date":"2019-09-08","arxiv_id":"1909.03493","repositories_listed":0,"syntology":null},{"url":null,"slug":"deleter-leveraging-bert-to-perform","title":"Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression","date":"2019-09-07","arxiv_id":"1909.03223","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-with-sentence-embeddings-pre","title":"Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records","date":"2019-09-06","arxiv_id":"1909.03044","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-and-learning-a-dependency-enhanced","title":"Extracting and Learning a Dependency-Enhanced Type Lexicon for Dutch","date":"2019-09-06","arxiv_id":"1909.02955","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-examples-with-difficult-common","title":"Robustness to Modification with Shared Words in Paraphrase Identification","date":"2019-09-05","arxiv_id":"1909.02560","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-aware-aspect-level-sentiment","title":"Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks","date":"2019-09-05","arxiv_id":"1909.02606","repositories_listed":0,"syntology":null},{"url":null,"slug":"transsent-towards-generation-of-structured","title":"TransSent: Towards Generation of Structured Sentences with Discourse Marker","date":"2019-09-05","arxiv_id":"1909.05364","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-entity-driven-framework-for-abstractive","title":"An Entity-Driven Framework for Abstractive Summarization","date":"2019-09-04","arxiv_id":"1909.02059","repositories_listed":0,"syntology":null},{"url":null,"slug":"simpler-and-faster-learning-of-adaptive","title":"Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation","date":"2019-09-04","arxiv_id":"1909.01559","repositories_listed":0,"syntology":null},{"url":null,"slug":"annotation-and-classification-of-sentence-1","title":"Annotation and Classification of Sentence-level Revision Improvement","date":"2019-09-03","arxiv_id":"1909.05309","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-pairwise-multi-perspective","title":"Attention-based Pairwise Multi-Perspective Convolutional Neural Network for Answer Selection in Question Answering","date":"2019-09-03","arxiv_id":"1909.01059","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-embeddings-for-relational-data","title":"Local Embeddings for Relational Data Integration","date":"2019-09-03","arxiv_id":"1909.01120","repositories_listed":0,"syntology":null},{"url":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","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-context-aware-neural-machine","title":"Improving Context-aware Neural Machine Translation with Target-side Context","date":"2019-09-02","arxiv_id":"1909.00531","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-long-range-context-for-concurrent","title":"Modeling Long-Range Context for Concurrent Dialogue Acts Recognition","date":"2019-09-02","arxiv_id":"1909.00521","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-level-content-planning-and-style","title":"Sentence-Level Content Planning and Style Specification for Neural Text Generation","date":"2019-09-02","arxiv_id":"1909.00734","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-and-accurate-partially-deterministic","title":"A Fast and Accurate Partially Deterministic Morphological Analysis","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantum-like-approach-to-word-sense","title":"A Quantum-Like Approach to Word Sense Disambiguation","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-semantic-augmentation-of-word","title":"A study of semantic augmentation of word embeddings for extractive summarization","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-topic-based-sentence-representation-for","title":"A topic-based sentence representation for extractive text summarization","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-neural-coherence-model","title":"A Unified Neural Coherence Model","date":"2019-09-01","arxiv_id":"1909.00349","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-detection-of-translation-direction","title":"Automatic Detection of Translation Direction","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-hyponymy-with-positive","title":"Compositional Hyponymy with Positive Operators","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discourse-aware-hierarchical-attention","title":"Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discourse-based-approach-to-involvement-of","title":"Discourse-Based Approach to Involvement of Background Knowledge for Question Answering","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-natural-language-understanding","title":"Enhancing Natural Language Understanding through Cross-Modal Interaction: Meaning Recovery from Acoustically Noisy Speech","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-unsupervised-sentence-similarity","title":"Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-open-ie-for-deriving-multiple","title":"Exploiting Open IE for Deriving Multiple Premises Entailment Corpus","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-comparisons-of-sentence-encoder","title":"Higher-order Comparisons of Sentence Encoder Representations","date":"2019-09-01","arxiv_id":"1909.00303","repositories_listed":0,"syntology":null},{"url":null,"slug":"may-i-check-again-a-simple-but-efficient-way-1","title":"May I Check Again? — A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to French Legal Texts.","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-embedding-sentence-representation-for","title":"Meta-Embedding Sentence Representation for Textual Similarity","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"morphosyntactic-disambiguation-in-an","title":"Morphosyntactic Disambiguation in an Endangered Language Setting","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-probing-of-deep-pre-trained","title":"Multilingual Probing of Deep Pre-Trained Contextual Encoders","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"naive-regularizers-for-low-resource-neural","title":"Naive Regularizers for Low-Resource Neural Machine Translation","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-processing-in-policy","title":"Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-corpus-of-croatian-italian","title":"Parallel Corpus of Croatian-Italian Administrative Texts","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-sentence-retrieval-from-comparable","title":"Parallel Sentence Retrieval From Comparable Corpora for Biomedical Text Simplification","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parts-of-speech-tagging-for-kannada","title":"Parts of Speech Tagging for Kannada","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-attention-with-structural-position","title":"Self-Attention with Structural Position Representations","date":"2019-09-01","arxiv_id":"1909.00383","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-simplification-for-semantic-role","title":"Sentence Simplification for Semantic Role Labelling and Information Extraction","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tagger-for-polish-computer-mediated","title":"Tagger for Polish Computer Mediated Communication Texts","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tddiscourse-a-dataset-for-discourse-level","title":"TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-semantic-linguistic-features-in","title":"The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"toponym-detection-in-the-bio-medical-domain-a","title":"Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tweaks-and-tricks-for-word-embedding","title":"Tweaks and Tricks for Word Embedding Disruptions","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"validation-of-facts-against-textual-sources","title":"Validation of Facts Against Textual Sources","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-with-1","title":"Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization","date":"2019-08-31","arxiv_id":"1909.00141","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-noisy-labels-for-sentence-level","title":"Learning with Noisy Labels for Sentence-level Sentiment Classification","date":"2019-08-31","arxiv_id":"1909.00124","repositories_listed":0,"syntology":null},{"url":null,"slug":"wslln-weakly-supervised-natural-language","title":"WSLLN: Weakly Supervised Natural Language Localization Networks","date":"2019-08-31","arxiv_id":"1909.00239","repositories_listed":0,"syntology":null}],"record_sha256":"162947e7f888d32cc2820e119cbe6d541802a190a7abfb645cdeab3803fa8cb8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}