{"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/paraphrase-identification/papers/2","list_of":"/task/paraphrase-identification","task":"Paraphrase Identification","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":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,172],"of":172,"counts":{"archive_papers_tagged":172,"with_a_code_link":76,"where_syntology_ran_a_sample":22,"not_listed_spam_title":0,"listed":172,"listed_where_code_ran":22,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":19,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":19,"listed_every_run_a_failure_of_syntologys_instrument":3,"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/paraphrase-identification","prev":"/task/paraphrase-identification","next":null,"papers":[{"url":null,"slug":"xla-a-robust-unsupervised-data-augmentation","title":"XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inducing-alignment-structure-with-gated-graph","title":"Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching","date":"2020-10-15","arxiv_id":"2010.07668","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-early-than-late-fusing-topics-with","title":"Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification","date":"2020-07-22","arxiv_id":"2007.11314","repositories_listed":0,"syntology":null},{"url":null,"slug":"experiments-on-paraphrase-identification","title":"Experiments on Paraphrase Identification Using Quora Question Pairs Dataset","date":"2020-06-04","arxiv_id":"2006.02648","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointwise-paraphrase-appraisal-is-potentially","title":"Pointwise Paraphrase Appraisal is Potentially Problematic","date":"2020-05-25","arxiv_id":"2005.11996","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-adaptation-using-universal","title":"Cross-Lingual Adaptation Using Universal Dependencies","date":"2020-03-24","arxiv_id":"2003.10816","repositories_listed":0,"syntology":null},{"url":"/paper/trans-blstm-transformer-with-bidirectional","slug":"trans-blstm-transformer-with-bidirectional","title":"TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding","date":"2020-03-16","arxiv_id":"2003.07000","repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-text-with-deep-mutual-information","title":"Matching Text with Deep Mutual Information Estimation","date":"2020-03-09","arxiv_id":"2003.11521","repositories_listed":0,"syntology":null},{"url":"/paper/multi-task-sentence-encoding-model-for","slug":"multi-task-sentence-encoding-model-for","title":"Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems","date":"2019-11-18","arxiv_id":"1911.07405","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-gap-between-relevance-matching","title":"Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"original-semantics-oriented-attention-and","title":"Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching","date":"2019-11-01","arxiv_id":null,"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":"a-qualitative-evaluation-framework-for","title":"A Qualitative Evaluation Framework for Paraphrase Identification","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/structbert-incorporating-language-structures","slug":"structbert-incorporating-language-structures","title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding","date":"2019-08-13","arxiv_id":"1908.04577","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cross-sentence-latent-variable-model-for","title":"A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching","date":"2019-06-04","arxiv_id":"1906.01343","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-paraphrasing-without-translation","title":"Unsupervised Paraphrasing without Translation","date":"2019-05-29","arxiv_id":"1905.12752","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiresolution-graph-attention-networks-for","title":"Multiresolution Graph Attention Networks for Relevance Matching","date":"2019-02-27","arxiv_id":"1902.10580","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-selectively-transfer-reinforced","title":"Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching","date":"2018-12-30","arxiv_id":"1812.11561","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-stack-residual-affinity-networks-with","title":"Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences","date":"2018-10-06","arxiv_id":"1810.02938","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-bq-corpus-a-large-scale-domain-specific","title":"The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification","date":"2018-10-01","arxiv_id":null,"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":"/paper/cell-aware-stacked-lstms-for-modeling","slug":"cell-aware-stacked-lstms-for-modeling","title":"Cell-aware Stacked LSTMs for Modeling Sentences","date":"2018-09-07","arxiv_id":"1809.02279","repositories_listed":0,"syntology":null},{"url":null,"slug":"lcqmca-large-scale-chinese-question-matching","title":"LCQMC:A Large-scale Chinese Question Matching Corpus","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"element-wise-bilinear-interaction-for","title":"Element-wise Bilinear Interaction for Sentence Matching","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/semantic-sentence-matching-with-densely","slug":"semantic-sentence-matching-with-densely","title":"Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information","date":"2018-05-29","arxiv_id":"1805.11360","repositories_listed":0,"syntology":null},{"url":null,"slug":"spade-evaluation-dataset-for-monolingual","title":"SPADE: Evaluation Dataset for Monolingual Phrase Alignment","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-natural-language-sentences-with","title":"Matching Natural Language Sentences with Hierarchical Sentence Factorization","date":"2018-03-01","arxiv_id":"1803.00179","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-domain-relationships-for-transfer","title":"Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce","date":"2017-11-23","arxiv_id":"1711.08726","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-similarity-analysis-for-paraphrase","title":"Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/learning-context-sensitive-convolutional","slug":"learning-context-sensitive-convolutional","title":"Learning Context-Sensitive Convolutional Filters for Text Processing","date":"2017-09-25","arxiv_id":"1709.08294","repositories_listed":0,"syntology":null},{"url":null,"slug":"constructing-narrative-using-a-generative","title":"Constructing narrative using a generative model and continuous action policies","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-weighted-alignment-network-for-sentence","title":"Inter-Weighted Alignment Network for Sentence Pair Modeling","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/a-continuously-growing-dataset-of-sentential","slug":"a-continuously-growing-dataset-of-sentential","title":"A Continuously Growing Dataset of Sentential Paraphrases","date":"2017-08-01","arxiv_id":"1708.00391","repositories_listed":0,"syntology":null},{"url":null,"slug":"lim-lig-at-semeval-2017-task1-enhancing-the","title":"LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-social-science-publications-for-survey","title":"Mining Social Science Publications for Survey Variables","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-alignment-using-unfolding-recursive","title":"Sentence Alignment using Unfolding Recursive Autoencoders","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sts-uhh-at-semeval-2017-task-1-scoring","title":"STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/neural-paraphrase-identification-of-questions","slug":"neural-paraphrase-identification-of-questions","title":"Neural Paraphrase Identification of Questions with Noisy Pretraining","date":"2017-04-15","arxiv_id":"1704.04565","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-of-binary-and-gradient","title":"Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase","date":"2017-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reddit-temporal-n-gram-corpus-and-its","title":"Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-sentence-similarities-for-better","title":"Exploiting Sentence Similarities for Better Alignments","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"content-selection-through-paraphrase","title":"Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea","date":"2016-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asobek-at-semeval-2016-task-1-sentence","title":"ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fbk-hlt-mt-at-semeval-2016-task-1-cross","title":"FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-recognize-ancillary-information","title":"Learning to Recognize Ancillary Information for Automatic Paraphrase Identification","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uta-dlnlp-at-semeval-2016-task-12-deep","title":"UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-phrase-embedding-for","title":"Discriminative Phrase Embedding for Paraphrase Identification","date":"2016-04-02","arxiv_id":"1604.00503","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-impact-of-machine-translation","title":"Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-perspective-sentence-similarity","title":"Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-kernel-approach-for-learning-typed","title":"A Unified Kernel Approach for Learning Typed Sentence Rewritings","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multigrancnn-an-architecture-for-general","title":"MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"amrita_censemeval-2015-paraphrase-detection","title":"AMRITA\\_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fbk-hlt-an-effective-system-for-paraphrase","title":"FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in Twitter","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hltc-hkust-a-neural-network-paraphrase","title":"HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mitre-seven-systems-for-semantic-similarity","title":"MITRE: Seven Systems for Semantic Similarity in Tweets","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"paraphrase-identification-and-semantic","title":"Paraphrase Identification and Semantic Similarity in Twitter with Simple Features","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/semeval-2015-task-1-paraphrase-and-semantic","slug":"semeval-2015-task-1-paraphrase-and-semantic","title":"SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"twitter-paraphrase-identification-with-simple","title":"Twitter Paraphrase Identification with Simple Overlap Features and SVMs","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-optimal-quadratic-approach-to-monolingual","title":"An Optimal Quadratic Approach to Monolingual Paraphrase Alignment","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-exploration-of-embeddings-for-generalized","title":"An Exploration of Embeddings for Generalized Phrases","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-paraphrase-identification-corpora","title":"On Paraphrase Identification Corpora","date":"2014-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/discriminative-improvements-to-distributional","slug":"discriminative-improvements-to-distributional","title":"Discriminative Improvements to Distributional Sentence Similarity","date":"2013-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-markov-phrase-based-monolingual","title":"Semi-Markov Phrase-Based Monolingual Alignment","date":"2013-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-crowdsourcing-for-paraphrase","title":"Leveraging Crowdsourcing for Paraphrase Recognition","date":"2013-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semilar-the-semantic-similarity-toolkit","title":"SEMILAR: The Semantic Similarity Toolkit","date":"2013-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"limsiiles-basic-english-substitution-for","title":"LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2013-task-5-evaluating-phrasal","title":"SemEval-2013 Task 5: Evaluating Phrasal Semantics","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"umcc_dlsi-eps-paraphrases-detection-based-on","title":"UMCC\\_DLSI-(EPS): Paraphrases Detection Based on Semantic Distance","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sriubc-simple-similarity-features-for","title":"SRIUBC: Simple Similarity Features for Semantic Textual Similarity","date":"2012-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"string-re-writing-kernel","title":"String Re-writing Kernel","date":"2012-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"re-examining-machine-translation-metrics-for","title":"Re-examining Machine Translation Metrics for Paraphrase Identification","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reference-scope-identification-in-citing","title":"Reference Scope Identification in Citing Sentences","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"2304403e03fa07da1ea91092eff334ba54130429587833d9115d62874c181809","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}