Local and Global Context Modeling with Relation Matching Task for Dialog Act Recognition.

In dialog act recognition (DAR) of an utterance in a conversation, the prior studies have focused either on the global context using the whole utterances in the dialog, or the local context using the neighbouring utterance flow in the dialog. However, their methods attempt to deal with all types of dialogs indiscriminately. In this study, we propose a model to extract the local context information by an inter-utterance relation matching task (RMT), and a DAR framework to incorporate the local context information into a hierarchical network to fulfil both local and global context modeling. Extensive evaluations were conducted on a Mandarin dialog corpus and two benchmark English corpora. It is found that the different dialog types possess different window lengths for RMT, which is related to the length of subtopics in a given type of dialog. According to ablation experiments, the global information contributed more to the DAR in the hierarchical framework, while the contribution ratio of the local to the global context information was larger than 0.1. The results demonstrated that the proposed RMT and DAR framework significantly improved the DAR performance.