In conversation, speakers produce language incrementally, word by word, while continuously monitoring the appropriateness of their own contribution in the dynamically unfolding context of the conversation; and this often leads them to repair their own utterance on the fly. This real-time language processing capacity is furthermore crucial to the development of fluent and natural conversational AI. In this paper, we use a previously learned Dynamic Syntax grammar and the CHILDES corpus to develop, train and evaluate a probabilistic model for incremental generation where input to the model is a purely semantic generation goal concept in Type Theory with Records (TTR). We show that the model's output exactly matches the gold candidate in 78% of cases with a ROUGE-l score of 0.86. We further do a zero-shot evaluation of the ability of the same model to generate self-repairs when the generation goal changes mid-utterance. Automatic evaluation shows that the model can generate self-repairs correctly in 85% of cases. A small human evaluation confirms the naturalness and grammaticality of the generated self-repairs. Overall, these results further highlight the generalisation power of grammar-based models and lay the foundations for more controllable, and naturally interactive conversational AI systems.

翻译：在对话中，说话者逐词递增地生成语言，同时持续监控自身话语在动态展开的对话语境中的恰当性；这种机制常导致说话者即时修正自己的话语。这种实时语言处理能力对于开发流畅自然的对话式AI至关重要。本文利用先前习得的动态句法语法和CHILDES语料库，开发、训练并评估了一个用于增量生成的概率模型——该模型的输入是基于记录类型理论（TTR）的纯语义生成目标概念。结果表明，模型输出与黄金候选结果在78%的情况下完全匹配，ROUGE-l分数达0.86。我们进一步对同一模型在生成目标中途改变时自主产生自我修复的能力进行零样本评估。自动评估显示，模型在85%的情况下能正确生成自我修复。小规模人工评估验证了所生成自我修复的自然性和语法正确性。总体而言，这些结果进一步凸显了基于语法模型的泛化能力，为构建更可控且具有自然交互能力的对话式AI系统奠定了基础。