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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Lee, Lillian; Shi, Tianze. - : Underline Science Inc., 2021 BASE Show details 2 Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Lee, Lillian; Shi, Tianze. - : Underline Science Inc., 2021 BASE Show details 3 Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction ... Shi, Tianze; Lee, Lillian. - : arXiv, 2021 BASE Show details 4 Learning Syntax from Naturally-Occurring Bracketings ... Shi, Tianze; İrsoy, Ozan; Malioutov, Igor. - : arXiv, 2021 BASE Show details 5 Learning Syntax from Naturally-Occurring Bracketings ... NAACL 2021 2021; İrsoy, Ozan; Lee, Lillian. - : Underline Science Inc., 2021 BASE Show details 6 On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries ... Shi, Tianze; Zhao, Chen; Boyd-Graber, Jordan. - : arXiv, 2020 BASE Show details 7 Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions ... Zhang, Rui; Yu, Tao; Er, He Yang; Shim, Sungrok; Xue, Eric; Lin, Xi Victoria; Shi, Tianze; Xiong, Caiming; Socher, Richard; Radev, Dragomir. - : arXiv, 2019 Abstract: We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch. ... : EMNLP 2019 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1909.00786 https://arxiv.org/abs/1909.00786 BASE Hide details 8 CoNLL 2017 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details

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