NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics

• URL: http://arxiv.org/abs/2112.08726v1
• Date: Thu, 16 Dec 2021 09:22:54 GMT
• Title: NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
• Authors: Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi
• Abstract summary: We propose NeuroLogic A*esque, a decoding algorithm that incorporates estimates of future cost. We develop efficient lookaheads that are efficient for large-scale language models. Our approach achieves competitive baselines on five generation tasks, and new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation.
• Score: 73.96837492216204
• License: http://creativecommons.org/licenses/by/4.0/
• 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 generation. The improvements are particularly notable on tasks that require complex constraint satisfaction or in few-shot or zero-shot settings. NeuroLogic A*esque illustrates the power of decoding for improving and enabling new capabilities of large-scale language models.

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