{"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/91","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":91,"pages_in_order":108,"rows_per_page":100,"rows":[9001,9100],"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/90","next":"/task/sentence/papers/92","papers":[{"url":null,"slug":"unsupervised-parallel-sentence-extraction-1","title":"Unsupervised Parallel Sentence Extraction from Comparable Corpora","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-entity-reasoner-for-global-consistency","title":"Neural Entity Reasoner for Global Consistency in NER","date":"2018-09-30","arxiv_id":"1810.00347","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-morphing","title":"Text Morphing","date":"2018-09-30","arxiv_id":"1810.00341","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-robust-transferable-sentence","title":"Learning Robust, Transferable Sentence Representations for Text Classification","date":"2018-09-28","arxiv_id":"1810.00681","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-rational-entailment-for-propositional","title":"On Rational Entailment for Propositional Typicality Logic","date":"2018-09-28","arxiv_id":"1809.10946","repositories_listed":0,"syntology":null},{"url":null,"slug":"salsa-text-self-attentive-latent-space-based","title":"SALSA-TEXT : self attentive latent space based adversarial text generation","date":"2018-09-28","arxiv_id":"1809.11155","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-rule-of-three-abstractive-text","title":"The Rule of Three: Abstractive Text Summarization in Three Bullet Points","date":"2018-09-28","arxiv_id":"1809.10867","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-differentiable-self-disambiguated-sense","title":"A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"denoise-while-aggregating-collaborative","title":"Denoise while Aggregating: Collaborative Learning in Open-Domain Question Answering","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-structural-planning-for-generating","title":"Discrete Structural Planning for Generating Diverse Translations","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graphseq2seq-graph-sequence-to-sequence-for","title":"GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchically-structured-variational","title":"Hierarchically-Structured Variational Autoencoders for Long Text Generation","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"log-hyperbolic-cosine-loss-improves","title":"Log Hyperbolic Cosine Loss Improves Variational Auto-Encoder","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-density-and-similarity-of-task","title":"Measuring Density and Similarity of Task Relevant Information in Neural Representations","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nuts-network-for-unsupervised-telegraphic","title":"NUTS: Network for Unsupervised Telegraphic Summarization","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-document-representation-using","title":"Unsupervised Document Representation using Partition Word-Vectors Averaging","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vector-learning-for-cross-domain","title":"Vector Learning for Cross Domain Representations","date":"2018-09-27","arxiv_id":"1809.10312","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-sentence-embeddings-for-paraphrasing","title":"Semantic Sentence Embeddings for Paraphrasing and Text Summarization","date":"2018-09-26","arxiv_id":"1809.10267","repositories_listed":0,"syntology":null},{"url":null,"slug":"hindi-english-code-switching-speech-corpus","title":"Hindi-English Code-Switching Speech Corpus","date":"2018-09-24","arxiv_id":"1810.00662","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-level-fluency-evaluation-references","title":"Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!","date":"2018-09-24","arxiv_id":"1809.08731","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-summarization-as-tree-transduction-by","title":"Text Summarization as Tree Transduction by Top-Down TreeLSTM","date":"2018-09-24","arxiv_id":"1809.09096","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-and-evaluating-sparse-interpretable","title":"Learning and Evaluating Sparse Interpretable Sentence Embeddings","date":"2018-09-23","arxiv_id":"1809.08621","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascade-attention-network-for-person-search","title":"Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search","date":"2018-09-22","arxiv_id":"1809.08440","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-encoder-decoder-networks-for","title":"Attention-based Encoder-Decoder Networks for Spelling and Grammatical Error Correction","date":"2018-09-21","arxiv_id":"1810.00660","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-do-you-correct-run-on-sentences-its-not","title":"How do you correct run-on sentences it's not as easy as it seems","date":"2018-09-21","arxiv_id":"1809.08298","repositories_listed":0,"syntology":null},{"url":null,"slug":"paraphrase-detection-on-noisy-subtitles-in","title":"Paraphrase Detection on Noisy Subtitles in Six Languages","date":"2018-09-21","arxiv_id":"1809.07978","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automated-factchecking-developing-an","title":"Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection","date":"2018-09-21","arxiv_id":"1809.08193","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-the-argumenthood-of-english","title":"Predicting the Argumenthood of English Prepositional Phrases","date":"2018-09-20","arxiv_id":"1809.07889","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-visual-relationship-for-image","title":"Exploring Visual Relationship for Image Captioning","date":"2018-09-19","arxiv_id":"1809.07041","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-topic-conversational-models","title":"Latent Topic Conversational Models","date":"2018-09-19","arxiv_id":"1809.07070","repositories_listed":0,"syntology":null},{"url":null,"slug":"monolingual-sentence-matching-for-text","title":"Monolingual sentence matching for text simplification","date":"2018-09-19","arxiv_id":"1809.08703","repositories_listed":0,"syntology":null},{"url":null,"slug":"nicts-corpus-filtering-systems-for-the-wmt18","title":"NICT's Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task","date":"2018-09-19","arxiv_id":"1809.07043","repositories_listed":0,"syntology":null},{"url":"/paper/open-subtitles-paraphrase-corpus-for-six","slug":"open-subtitles-paraphrase-corpus-for-six","title":"Open Subtitles Paraphrase Corpus for Six Languages","date":"2018-09-17","arxiv_id":"1809.06142","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-transfer-through-multilingual-and","title":"Style Transfer Through Multilingual and Feedback-Based Back-Translation","date":"2018-09-17","arxiv_id":"1809.06284","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-multi-task-word-embeddings","title":"Meta-Embedding as Auxiliary Task Regularization","date":"2018-09-16","arxiv_id":"1809.05886","repositories_listed":0,"syntology":null},{"url":null,"slug":"macquarie-university-at-bioasq-6b-deep-1","title":"Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation","date":"2018-09-14","arxiv_id":"1809.05283","repositories_listed":0,"syntology":null},{"url":null,"slug":"awe-asymmetric-word-embedding-for-textual","title":"AWE: Asymmetric Word Embedding for Textual Entailment","date":"2018-09-11","arxiv_id":"1809.04047","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-lstm-learn-to-capture-agreement-the-case","title":"Can LSTM Learn to Capture Agreement? The Case of Basque","date":"2018-09-11","arxiv_id":"1809.04022","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-multimodal-representations-on","title":"Evaluating Multimodal Representations on Sentence Similarity: vSTS, Visual Semantic Textual Similarity Dataset","date":"2018-09-11","arxiv_id":"1809.03695","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-semantic-rationality-of-a-sentence","title":"Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet","date":"2018-09-11","arxiv_id":"1809.03999","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-can-linguistics-and-deep-learning","title":"What can linguistics and deep learning contribute to each other?","date":"2018-09-11","arxiv_id":"1809.04179","repositories_listed":0,"syntology":null},{"url":null,"slug":"filling-missing-paths-modeling-co-occurrences","title":"Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations","date":"2018-09-10","arxiv_id":"1809.03411","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-neural-generators-for-dialogue-learn","title":"Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?","date":"2018-09-09","arxiv_id":"1809.03015","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentive-semantic-role-labeling-with","title":"Attentive Semantic Role Labeling with Boundary Indicator","date":"2018-09-08","arxiv_id":"1809.02796","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-invertible-decoders-for","title":"Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning","date":"2018-09-08","arxiv_id":"1809.02731","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-generation-of-diverse-questions-using","title":"Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features","date":"2018-09-07","arxiv_id":"1809.02637","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-cross-lingual-word-embedding-by","title":"Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models","date":"2018-09-07","arxiv_id":"1809.02306","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-if-we-simply-swap-the-two-text-fragments","title":"What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks","date":"2018-09-07","arxiv_id":"1809.02719","repositories_listed":0,"syntology":null},{"url":null,"slug":"82-treebanks-34-models-universal-dependency","title":"82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models","date":"2018-09-06","arxiv_id":"1809.02237","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-down-tree-structured-decoding-with","title":"Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing","date":"2018-09-06","arxiv_id":"1809.01854","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-neural-sequence-model-with-multiple","title":"A Novel Neural Sequence Model with Multiple Attentions for Word Sense Disambiguation","date":"2018-09-04","arxiv_id":"1809.01074","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointwise-hsic-a-linear-time-kernelized-co","title":"Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions","date":"2018-09-04","arxiv_id":"1809.00800","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-argument-mining-for-discussion","title":"End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture","date":"2018-09-03","arxiv_id":"1809.00563","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-structured-self-attentions-for","title":"Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction","date":"2018-09-03","arxiv_id":"1809.00699","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-gap-filling-as-a-cheaper","title":"Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting","date":"2018-09-02","arxiv_id":"1809.00315","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-error-propagation-in-neural-machine","title":"Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter","date":"2018-09-01","arxiv_id":"1809.00120","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-conditional-cross-entropy-filtering-of","title":"Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora","date":"2018-09-01","arxiv_id":"1809.00197","repositories_listed":0,"syntology":null},{"url":null,"slug":"find-and-focus-retrieve-and-localize-video","title":"Find and Focus: Retrieve and Localize Video Events with Natural Language Queries","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"microsofts-submission-to-the-wmt2018-news","title":"Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data","date":"2018-09-01","arxiv_id":"1809.00196","repositories_listed":0,"syntology":null},{"url":null,"slug":"nneval-neural-network-based-evaluation-metric","title":"NNEval: Neural Network based Evaluation Metric for Image Captioning","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-anaphora-with-non-nominal-antecedents","title":"Survey: Anaphora With Non-nominal Antecedents in Computational Linguistics: a Survey","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-neural-network-sentence-level","title":"A Deep Neural Network Sentence Level Classification Method with Context Information","date":"2018-08-31","arxiv_id":"1809.00934","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-language-models-understand-anything-on-the","title":"Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items","date":"2018-08-31","arxiv_id":"1808.10627","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-syntactic-neural-machine","title":"Multi-Source Syntactic Neural Machine Translation","date":"2018-08-30","arxiv_id":"1808.10267","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-compositional-denotational-semantics","title":"Neural Compositional Denotational Semantics for Question Answering","date":"2018-08-29","arxiv_id":"1808.09942","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-helpfulness-prediction-with-embedding","title":"Review Helpfulness Prediction with Embedding-Gated CNN","date":"2018-08-29","arxiv_id":"1808.09896","repositories_listed":0,"syntology":null},{"url":"/paper/wikiatomicedits-a-multilingual-corpus-of","slug":"wikiatomicedits-a-multilingual-corpus-of","title":"WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse","date":"2018-08-28","arxiv_id":"1808.09422","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-domain-adjacent-instances-for","title":"Identifying Domain Adjacent Instances for Semantic Parsers","date":"2018-08-26","arxiv_id":"1808.08626","repositories_listed":0,"syntology":null},{"url":null,"slug":"paraphrases-as-foreign-languages-in","title":"Paraphrases as Foreign Languages in Multilingual Neural Machine Translation","date":"2018-08-25","arxiv_id":"1808.08438","repositories_listed":0,"syntology":null},{"url":null,"slug":"mapping-text-to-knowledge-graph-entities","title":"Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs","date":"2018-08-23","arxiv_id":"1808.07724","repositories_listed":0,"syntology":null},{"url":null,"slug":"role-of-intonation-in-scoring-spoken-english","title":"Role of Intonation in Scoring Spoken English","date":"2018-08-23","arxiv_id":"1808.07688","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-transfer-as-unsupervised-machine","title":"Style Transfer as Unsupervised Machine Translation","date":"2018-08-23","arxiv_id":"1808.07894","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-importance-of-generation-order-in","title":"The Importance of Generation Order in Language Modeling","date":"2018-08-23","arxiv_id":"1808.07910","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-good-representations-of-emotions-for","title":"Finding Good Representations of Emotions for Text Classification","date":"2018-08-22","arxiv_id":"1808.07235","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-matching-models-with-contextualized","title":"Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection","date":"2018-08-22","arxiv_id":"1808.07244","repositories_listed":0,"syntology":null},{"url":"/paper/neural-latent-extractive-document","slug":"neural-latent-extractive-document","title":"Neural Latent Extractive Document Summarization","date":"2018-08-22","arxiv_id":"1808.07187","repositories_listed":0,"syntology":null},{"url":null,"slug":"sarcasm-analysis-using-conversation-context","title":"Sarcasm Analysis using Conversation Context","date":"2018-08-22","arxiv_id":"1808.07531","repositories_listed":0,"syntology":null},{"url":null,"slug":"switchout-an-efficient-data-augmentation","title":"SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation","date":"2018-08-22","arxiv_id":"1808.07512","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-pointer-network-for-product","title":"Multi-Source Pointer Network for Product Title Summarization","date":"2018-08-21","arxiv_id":"1808.06885","repositories_listed":0,"syntology":null},{"url":"/paper/neural-relation-extraction-via-inner-sentence","slug":"neural-relation-extraction-via-inner-sentence","title":"Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning","date":"2018-08-21","arxiv_id":"1808.06738","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-detection-of-vague-words-and","title":"Automatic Detection of Vague Words and Sentences in Privacy Policies","date":"2018-08-19","arxiv_id":"1808.06219","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-compose-over-tree-structures-via","title":"Learning to Compose over Tree Structures via POS Tags","date":"2018-08-18","arxiv_id":"1808.06075","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-neural-networks-for-cross-lingual","title":"Adversarial Neural Networks for Cross-lingual Sequence Tagging","date":"2018-08-14","arxiv_id":"1808.04736","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-structural-planning-for-neural","title":"Discrete Structural Planning for Neural Machine Translation","date":"2018-08-14","arxiv_id":"1808.04525","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-much-reading-does-reading-comprehension","title":"How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks","date":"2018-08-14","arxiv_id":"1808.04926","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-down-tree-structured-text-generation","title":"Top-Down Tree Structured Text Generation","date":"2018-08-14","arxiv_id":"1808.04865","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-style-transfer-from-sentences-with","title":"Language Style Transfer from Sentences with Arbitrary Unknown Styles","date":"2018-08-13","arxiv_id":"1808.04071","repositories_listed":0,"syntology":null},{"url":null,"slug":"regmapr-text-matching-made-easy","title":"REGMAPR - Text Matching Made Easy","date":"2018-08-13","arxiv_id":"1808.04343","repositories_listed":0,"syntology":null},{"url":null,"slug":"fake-sentence-detection-as-a-training-task","title":"Fake Sentence Detection as a Training Task for Sentence Encoding","date":"2018-08-11","arxiv_id":"1808.03840","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hassle-free-machine-learning-method-for","title":"A Hassle-Free Machine Learning Method for Cohort Selection of Clinical Trials","date":"2018-08-10","arxiv_id":"1808.04694","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-of-sentence","title":"Unsupervised Learning of Sentence Representations Using Sequence Consistency","date":"2018-08-10","arxiv_id":"1808.04217","repositories_listed":0,"syntology":null},{"url":null,"slug":"arithmetic-word-problem-solver-using-frame","title":"Arithmetic Word Problem Solver using Frame Identification","date":"2018-08-09","arxiv_id":"1808.03028","repositories_listed":0,"syntology":null},{"url":null,"slug":"code-mixed-sentiment-analysis-using-machine","title":"Code-Mixed Sentiment Analysis Using Machine Learning and Neural Network Approaches","date":"2018-08-09","arxiv_id":"1808.03299","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialog-context-aware-end-to-end-speech","title":"Dialog-context aware end-to-end speech recognition","date":"2018-08-07","arxiv_id":"1808.02171","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-did-the-discussion-go-discourse-act","title":"How did the discussion go: Discourse act classification in social media conversations","date":"2018-08-07","arxiv_id":"1808.02290","repositories_listed":0,"syntology":null},{"url":null,"slug":"abstractive-summarization-improved-by-wordnet","title":"Abstractive Summarization Improved by WordNet-based Extractive Sentences","date":"2018-08-04","arxiv_id":"1808.01426","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-chinese-writing-correction-system-for","title":"A Chinese Writing Correction System for Learning Chinese as a Foreign Language","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compositional-bayesian-semantics-for","title":"A Compositional Bayesian Semantics for Natural Language","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cross-lingual-messenger-with-keyword","title":"A Cross-lingual Messenger with Keyword Searchable Phrases for the Travel Domain","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-full-end-to-end-semantic-role-labeler","title":"A Full End-to-End Semantic Role Labeler, Syntactic-agnostic Over Syntactic-aware?","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"c5b788df8afb7e0d35eb1286a13877cc71874f9ac4236b7ea7a5c681e0f2d0c2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}