Utterance Position-Aware Dialogue Act Recognition
Yuki Yano | Akihiro Tamura | Takashi Ninomiya | Hiroaki Obayashi
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance’s absolute or relative position. The proposed approach is inspired by the observation that some dialogue acts have tendencies of occurrence positions. The evaluations on the Switchboard corpus show that the proposed positional encoding of utterances statistically significantly improves the performance of DAR.