Modeling Long-Range Context for Concurrent Dialogue Acts Recognition

TL;DR

This paper introduces an adapted CRNN model that effectively captures long-range context to improve recognition of multiple dialogue acts within a single utterance, addressing complex dependencies in dialogue analysis.

Contribution

The paper proposes a novel CRNN-based approach specifically designed for Concurrent Dialogue Acts recognition, handling long utterances and multiple DA labels per utterance.

Findings

Model significantly outperforms existing methods on a tech forum dataset.

Effectively captures long-range contextual dependencies in dialogues.

Handles multiple dialogue acts within a single utterance.

Abstract

In dialogues, an utterance is a chain of consecutive sentences produced by one speaker which ranges from a short sentence to a thousand-word post. When studying dialogues at the utterance level, it is not uncommon that an utterance would serve multiple functions. For instance, "Thank you. It works great." expresses both gratitude and positive feedback in the same utterance. Multiple dialogue acts (DA) for one utterance breeds complex dependencies across dialogue turns. Therefore, DA recognition challenges a model's predictive power over long utterances and complex DA context. We term this problem Concurrent Dialogue Acts (CDA) recognition. Previous work on DA recognition either assumes one DA per utterance or fails to realize the sequential nature of dialogues. In this paper, we present an adapted Convolutional Recurrent Neural Network (CRNN) which models the interactions between utterances of long-range context. Our model significantly outperforms existing work on CDA recognition on a tech forum dataset.