Human-like informative conversations: Better acknowledgements using conditional mutual information
Ashwin Paranjape, Christopher D. Manning · 2021
This work aims to build a dialogue agent that can weave new factual content into conversations as naturally as humans.We draw insights from linguistic principles of conversational analysis and annotate human-human conversations from the Switchboard Dialog Act Corpus to examine humans strategies for acknowledgement, transition, detail selection and presentation.When current chatbots (explicitly provided with new factual content) introduce facts into a conversation, their generated responses do not acknowledge the prior turns.This is because models trained with two contexts -new factual content and conversational history -generate responses that are non-specific w.r.t.one of the contexts, typically the conversational history.We show that specificity w.r.t.conversational history is better captured by pointwise conditional mutual information (pcmi h ) than by the established use of pointwise mutual information (pmi).Our proposed method, Fused-PCMI, trades off pmi for pcmi h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time.Human evaluators also judge responses with higher pcmi h better at acknowledgement 74% of the time.The results demonstrate that systems mimicking human conversational traits (in this case acknowledgement) improve overall quality and more broadly illustrate the utility of linguistic principles in improving dialogue agents.