EM Pre-training for Multi-party Dialogue Response Generation
Yiyang Li Hai Zhao
Dialogue response generation requires an agent to generate a response
according to the current dialogue history, in terms of which two-party
dialogues have been well studied, but leaving a great gap for multi-party
dialogues at the same time. Different from two-party dialogues where each
response is a direct reply to its previous utterance, the addressee of a
response utterance should be specified before it is generated in the
multi-party scenario. Thanks to the huge amount of two-party conversational
data, various pre-trained language models for two-party dialogue response
generation have been proposed. However, due to the lack of annotated addressee
labels in multi-party dialogue datasets, it is hard to use them to pre-train a
response generation model for multi-party dialogues. To tackle this obstacle,
we propose an Expectation-Maximization (EM) approach that iteratively performs
the expectation steps to generate addressee labels, and the maximization steps
to optimize a response generation model. Theoretical analyses and extensive
experiments have justified the feasibility and effectiveness of our proposed
method.