AutoTemplate：一种用于词汇约束文本生成的简单方法
AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation
AI总结：
本文提出 AutoTemplate 框架，通过“模板生成＋占位符词汇化”两阶段方法生成满足硬词汇约束的文本，并在关键词到句子生成和实体引导摘要任务上优于强基线。
摘要：
词汇约束文本生成是约束文本生成任务之一，旨在生成覆盖所有给定约束词汇的文本。现有方法通过词汇约束束搜索算法或采用非自回归解码的专用模型来解决该问题，但在生成文本质量与硬约束满足之间存在权衡。我们提出 AutoTemplate，这是一个简单而有效的词汇约束文本生成框架，分为模板生成和词汇化两个任务。模板生成用于生成带有占位符的文本，而词汇化则将这些占位符替换为约束词汇，以完成词汇约束文本生成。我们在两项任务上进行了实验：关键词到句子生成以及实体引导摘要。实验结果表明，AutoTemplate 在两项任务上均优于具有竞争力的基线，同时满足硬词汇约束。代码可在 https://github.com/megagonlabs/autotemplate 获取。
英文摘要：
Lexically constrained text generation is one of the constrained text generation tasks, which aims to generate text that covers all the given constraint lexicons. While the existing approaches tackle this problem using a lexically constrained beam search algorithm or dedicated model using non-autoregressive decoding, there is a trade-off between the generated text quality and the hard constraint satisfaction. We introduce AutoTemplate, a simple yet effective lexically constrained text generation framework divided into template generation and lexicalization tasks. The template generation is to generate the text with the placeholders, and lexicalization replaces them into the constraint lexicons to perform lexically constrained text generation. We conducted the experiments on two tasks: keywords-to-sentence generations and entity-guided summarization. Experimental results show that the AutoTemplate outperforms the competitive baselines on both tasks while satisfying the hard lexical constraints. The code is available at https://github.com/megagonlabs/autotemplate
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
