论文标题
整个真理，除了真理：忠实而可控的对话响应，并通过数据流转导和限制解码
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
论文作者
论文摘要
在现实世界中的对话系统中，生成的文本必须是真实的和信息丰富的，同时保持流利并遵守规定的风格。对于语言生成中的两个主要范式而言，同时满足这些约束是很难的：神经语言建模和基于规则的生成。我们描述了对话响应产生的混合体系结构，结合了这两个范式的优势。该体系结构的第一个组成部分是一种基于规则的内容选择模型，该模型使用称为DataFlow Transduction的新正式框架定义，该框架使用声明性规则将对话代理的动作及其结果（表示为数据流图表示）转换为代表上下文可接受响应的空间的无上下文语法。第二个组件是一种受约束的解码过程，它使用这些语法来限制神经语言模型的输出，从而选择流利的话语。我们的实验表明，该系统在人类对流利，相关性和真实性的评估中的表现都优于基于规则的方法。
In a real-world dialogue system, generated text must be truthful and informative while remaining fluent and adhering to a prescribed style. Satisfying these constraints simultaneously is difficult for the two predominant paradigms in language generation: neural language modeling and rule-based generation. We describe a hybrid architecture for dialogue response generation that combines the strengths of both paradigms. The first component of this architecture is a rule-based content selection model defined using a new formal framework called dataflow transduction, which uses declarative rules to transduce a dialogue agent's actions and their results (represented as dataflow graphs) into context-free grammars representing the space of contextually acceptable responses. The second component is a constrained decoding procedure that uses these grammars to constrain the output of a neural language model, which selects fluent utterances. Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
