Utterance Position-Aware Dialogue Act Recognition (2021)

Abstract: 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.

## introduction the recognition of dialogue acts (das), which represent the intention or function of each utterance in a dialogue, is useful for various dialogue applications, such as dialogue systems and dialogue summarization. recently, neural network-(nn-)based approaches have become dominant in dialogue act recognition (dar) because nn-based models obtained higher performance than other approaches. most existing nn-based dar models treat dar as a sequence labeling problem, where utterances are first encoded with a hierarchical recurrent neural network (rnn), and subsequently, the sequence of dar labels is identified from the encoded representations using the conditional random field (crf) @xcite @xcite . these crf-based models can capture the local dependencies of da sequences; however, they cannot model global dependencies due to the first-order markov assumption. to alleviate this problem, @xcite proposed a sequence-to-sequence (seq2seq) architecture for dar, which could learn global dependencies, and achieve the state-of-theart performance. we focus on this work and employ it as the basis of our proposed model. existing nn-based dar models focus on the context of a dialogue and dependencies between das but do not focus on the position of an utterance. however, we have found that some das have tendencies of occurrence positions. for example, the "open-question" da in the switchboard corpus tends to appear at the beginning of a telephonic conversation. inspired by this observation, this study proposes an utterance position-aware approach for a nnbased dar model, which explicitly encodes the position of an utterance by positional encoding and generates da label sequences based on the hidden vectors augmented with the positional encoding of utterance. in particular, we implement two types of positional encodings of utterances: (1) sinusoidal positional encoding used in transformer neural machine translation @xcite , which represents the absolute position of each utterance; the evaluations on the switchboard corpus @xcite , which is one of the most popular benchmark datasets in dar, show that the performance of dar is statistically significantly improved by incorporating the proposed positional encoding of utterances (up to +0.31 precision). our analysis demonstrates that the proposed model statistically significantly improves the performance of long dialogues. word layer utterance layer i-th utterance hierarchical encoder decoder hard guided attention positional encoding { } figure 1: overview of the proposed model. the red dashed rectangle represents the proposed positional encoding of utterances.