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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 – 5 of 5 1 Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models ... 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; Bhagavatula, Chandra; Choi, Yejin. - : Underline Science Inc., 2021 BASE Show details 2 DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts ... 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; Bhagavatula, Chandra; Choi, Yejin. - : Underline Science Inc., 2021 BASE Show details 3 NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics ... Lu, Ximing; Welleck, Sean; West, Peter; Jiang, Liwei; Kasai, Jungo; Khashabi, Daniel; Bras, Ronan Le; Qin, Lianhui; Yu, Youngjae; Zellers, Rowan; Smith, Noah A.; Choi, Yejin. - : arXiv, 2021 Abstract: The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing inspiration from the A* search algorithm, we propose NeuroLogic A*esque, a decoding algorithm that incorporates heuristic estimates of future cost. We develop efficient lookahead heuristics that are efficient for large-scale language models, making our method a drop-in replacement for common techniques such as beam search and top-k sampling. To enable constrained generation, we build on NeuroLogic decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A*esque estimates of future constraint satisfaction. Our approach outperforms competitive baselines on five generation tasks, and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2112.08726 https://arxiv.org/abs/2112.08726 BASE Hide details 4 NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints ... NAACL 2021 2021; Lu, Ximing. - : Underline Science Inc., 2021 BASE Show details 5 NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints ... Lu, Ximing; West, Peter; Zellers, Rowan. - : arXiv, 2020 BASE Show details

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