Learning to generate and corr- uh I mean repair language in real-time
Arash Eshghi Arash Ashrafzadeh
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.