Lexicon-based constrained decoding approaches aim to control the meaning or style of the generated text through certain target concepts. Existing approaches over-focus the targets themselves, leading to a lack of high-level reasoning about how to achieve them. However, human usually tackles tasks by following certain rules that not only focuses on the targets but also on semantically relevant concepts that induce the occurrence of targets. In this work, we present DECIDER, a rule-controllable decoding strategy for constrained language generation inspired by dual-system cognitive theory. Specifically, in DECIDER, a pre-trained language model (PLM) is equiped with a logic reasoner that takes high-level rules as input. Then, the DECIDER allows rule signals to flow into the PLM at each decoding step. Extensive experimental results demonstrate that DECIDER can effectively follow given rules to guide generation direction toward the targets in a more human-like manner.

通过受到双系统认知理论启发的 DECIDER 规则可控解码策略，本文提出了一种基于词典的约束解码方法，旨在通过特定目标概念来控制生成文本的意义或风格，实验证明 DECIDER 可以以更接近人类的方式遵循给定的规则引导生成方向，从而有效地控制生成结果。

DECIDER: 一种基于模仿双系统认知理论的规则可控解码策略