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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. - : arXiv, 2021 BASE Show 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; Bras, Ronan Le; Bhagavatula, Chandra; Choi, Yejin. - : arXiv, 2020 Abstract: Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples. We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models -- supervised or not -- to generate fluent text while satisfying complex lexical constraints. Our approach is powerful yet efficient. It handles any set of lexical constraints that is expressible under predicate logic, while its asymptotic runtime is equivalent to conventional beam search. Empirical results on four benchmarks show that NeuroLogic Decoding outperforms previous approaches, including algorithms that handle a subset of our constraints. Moreover, we find that unsupervised ... : NAACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.12884 https://dx.doi.org/10.48550/arxiv.2010.12884 BASE Hide details

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