Semantic Representation for Dialogue Modeling

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 4430–4445 August 1–6, 2021. ©2021 Association for Computational Linguistics 4430 Semantic Representation for Dialogue Modeling Xuefeng Bai♠♥ , Yulong Chen♠♥ , Linfeng Song♣ , Yue Zhang♥♦ ♠ Zhejiang University, China ♥ School of Engineering, Westlake University, China ♣ Tencent AI Lab, Bellevue, WA, USA ♦ Institute of Advanced Technology, Westlake Institute for Advanced Study, China Abstract Although neural models have achieved com- petitive results in dialogue systems, they have shown limited ability in representing core se- mantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue mod- eling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dia- logue understanding and response generation tasks show the superiority of our model. To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling. 1 Introduction Dialogue systems have received increasing re- search attention (Wen et al., 2015; Serban et al., 2017; Bao et al., 2020), with much recent work focusing on social chats (Ritter et al., 2011; Li et al., 2017) and task-oriented dialogues (Wen et al., 2017; Dinan et al., 2019). There are two salient subtasks in dialogue modeling, namely dialogue understanding (Choi et al., 2018; Reddy et al., 2019; Yu et al., 2020) and response generation (Li et al., 2017; Budzianowski et al., 2018). The former refers to understanding of semantic and discourse details in a dialogue history, and the latter concerns making a fluent, novel and coherent utterance. The current state-of-the-art methods employ neural networks and end-to-end training (Sutskever et al., 2014; Bahdanau et al., 2015) for dialogue modeling. For instance, sequence-to-sequence models have been used to encode a dialogue history, before directly synthesizing the next utterance (Vinyals and Le, 2015; Wen et al., 2017; Bao et al., Dialogue History: … SPEAKER-1 : Recently, I’ve been obsessed with horror films. SPEAKER-2 : Oh, how can you be infatuated with horror films? They’re so scary . SPEAKER-1 : Yeah, you are right I used to not watch horror films, but after seeing Silence of the Lamb with Mike last month, I fell in love with them. SPEAKER-2 : It’s amazing. But if I were you, I wouldn't have the courage to watch the first one. SPEAKER-1 : But it's really exciting . Ground-Truth: Maybe, but I would rather watch romance, science fiction, crime or even disaster movie instead of a horror picture… Transformer: Great. I’m looking forward to it. I just can’t keep away from the food that I saw. Figure 1: A conversation from DailyDialog. Some important contents are marked with squares. 2020). Despite giving strong empirical results, neural models can suffer from spurious feature associations in their neural semantic …

Content truncated to 500 words