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Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders
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Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
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Towards Robustness of Text-to-SQL Models against Synonym Substitution
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Abstract:
Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matching between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism. In this work, we investigate the robustness of text-to-SQL models to synonym substitution. In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases. We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks. Finally, we present two categories of approaches to improve the model robustness. The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training. We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective.
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Keyword:
cs.CL
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URL: https://qmro.qmul.ac.uk/xmlui/handle/123456789/72701
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Study of central exclusive [Image: see text] production in proton-proton collisions at [Formula: see text] and 13TeV
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In: Eur Phys J C Part Fields (2020)
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The relationship between English proficiency and humour appreciation among English L1 users and Chinese L2 users of English
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The flowering of positive psychology in Foreign Language Teaching and Acquisition research
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Exploiting future word contexts in neural network language models for speech recognition
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Disparities in Diabetes Care Quality by English Language Preference in Community Health Centers
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In: Leung, LB; Vargas-Bustamante, A; Martinez, AE; Chen, X; & Rodriguez, HP. (2018). Disparities in Diabetes Care Quality by English Language Preference in Community Health Centers. Health Services Research, 53(1), 509 - 531. doi:10.1111/1475-6773.12590. UCLA: Retrieved from: http://www.escholarship.org/uc/item/40x4d7fn (2018)
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Phonetic and graphemic systems for multi-genre broadcast transcription ...
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Phonetic and graphemic systems for multi-genre broadcast transcription
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Wang, Yu; Chen, X; Gales, Mark. - : IEEE, 2018. : https://ieeexplore.ieee.org/document/8462353, 2018. : ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2018
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Search for dark matter produced in association with heavy-flavor quark pairs in proton-proton collisions at [Formula: see text]
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Developing Universal Dependencies for Mandarin Chinese
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In: The 12th Workshop on Asian Language Resources ; https://halshs.archives-ouvertes.fr/halshs-01509329 ; The 12th Workshop on Asian Language Resources, 2016, Osaka, Japan (2016)
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Investigation of Back-off Based Interpolation Between Recurrent Neural Network and N-gram Language Models (Author's Manuscript)
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Classifying Syntactic Categories in the Chinese Dependency Network
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In: Depling 2015 ; https://halshs.archives-ouvertes.fr/halshs-01509117 ; Depling 2015, 2015, Uppsala, Sweden (2015)
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Robust excitation-based features for Automatic Speech Recognition
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Drugman, T; Stylianou, Y; Chen, L. - : IEEE, 2015. : ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015
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Paraphrastic recurrent neural network language models
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Liu, X; Chen, X; Gales, Mark. - : IEEE, 2015. : ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015
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Measurement of the muon reconstruction performance of the ATLAS detector using 2011 and 2012 LHC proton-proton collision data.
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In: Symplectic Elements at Oxford ; Europe PubMed Central ; PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) ; Web of Science (Lite) (http://apps.webofknowledge.com/summary.do) ; Scopus (http://www.scopus.com/home.url) ; CrossRef (2014)
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