A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection
Qian Lin, Souvik Kundu, Hwee Tou Ng · 2020
Ensuring smooth communication is essential in a chat-oriented dialogue system, so that a user can obtain meaningful responses through interactions with the system.Most prior work on dialogue research does not focus on preventing dialogue breakdown.One of the major challenges is that a dialogue system may generate an undesired utterance leading to a dialogue breakdown, which degrades the overall interaction quality.Hence, it is crucial for a machine to detect dialogue breakdowns in an ongoing conversation.In this paper, we propose a novel dialogue breakdown detection model that jointly incorporates a pretrained cross-lingual language model and a co-attention network.Our proposed model leverages effective word embeddings trained on one hundred different languages to generate contextualized representations.Co-attention aims to capture the interaction between the latest utterance and the conversation history, and thereby determines whether the latest utterance causes a dialogue breakdown.Experimental results show that our proposed model outperforms all previous approaches on all evaluation metrics in both the Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 (DBDC4 at IWSDS2019).