论文标题
通过组合约束满意度来产生语言：树木搜索增强了蒙特卡洛的方法
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
论文作者
论文摘要
在复杂的约束下生成自然语言是针对可控文本生成的原则提法。我们提出一个框架，允许对句子生成的组合约束规范。我们提出了TSMH，这是一种有效的方法，可以在满足约束的同时，就预训练的语言模型产生高似然句子。我们的方法非常灵活，不需要特定于任务的培训，并且利用有效的约束满意度解决方案。为了更好地处理组合约束，将树搜索算法嵌入了马尔可夫链蒙特卡洛（MCMC）的建议过程中，以探索满足更多约束的候选人。与现有的MCMC方法相比，我们的抽样方法具有更好的混合性能。实验表明，TSMH在多语言生成任务上取得了一致和显着的改进。
Generating natural language under complex constraints is a principled formulation towards controllable text generation. We present a framework to allow specification of combinatorial constraints for sentence generation. We propose TSMH, an efficient method to generate high likelihood sentences with respect to a pre-trained language model while satisfying the constraints. Our approach is highly flexible, requires no task-specific training, and leverages efficient constraint satisfaction solving techniques. To better handle the combinatorial constraints, a tree search algorithm is embedded into the proposal process of the Markov chain Monte Carlo (MCMC) to explore candidates that satisfy more constraints. Compared to existing MCMC approaches, our sampling approach has a better mixing performance. Experiments show that TSMH achieves consistent and significant improvement on multiple language generation tasks.
