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.

在这篇论文中，我们使用先前学习的动态句法语法和 CHILDES 语料库开发、训练和评估了一个概率模型，该模型用于增量生成，其中模型的输入是纯语义生成目标概念（TTR）。我们证明了该模型的输出与黄金候选项的准确匹配率为 78％，ROUGE-l 评分为 0.86。我们进一步对同一模型在生成目标在发话过程中发生变化时产生自我修复的能力进行了零次评估。自动评估显示模型能够在 85％的情况下正确生成自我修复。小规模的人工评估确认了生成的自我修复的自然性和语法性。总体而言，这些结果进一步突显了基于语法的模型的泛化能力，并为更可控和自然交互的对话型人工智能系统奠定了基础。