A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 9207–9219, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 9207 A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning Yichi Zhang1, Zhijian Ou1∗, Min Hu2, Junlan Feng2 1 Speech Processing and Machine Intelligence Lab, Tsinghua University, Beijing, China 2 China Mobile Research Institute, Beijing, China zhangyic17@tsinghua.org.cn, ozj@tsinghua.edu.cn Abstract Structured belief states are crucial for user goal tracking and database query in task-oriented dialog systems. However, training belief track- ers often requires expensive turn-level anno- tations of every user utterance. In this pa- per we aim at alleviating the reliance on be- lief state labels in building end-to-end dialog systems, by leveraging unlabeled dialog data towards semi-supervised learning. We pro- pose a probabilistic dialog model, called the LAtent BElief State (LABES) model, where belief states are represented as discrete la- tent variables and jointly modeled with sys- tem responses given user inputs. Such la- tent variable modeling enables us to develop semi-supervised learning under the principled variational learning framework. Furthermore, we introduce LABES-S2S, which is a copy- augmented Seq2Seq model instantiation of LABES1. In supervised experiments, LABES- S2S obtains strong results on three benchmark datasets of different scales. In utilizing un- labeled dialog data, semi-supervised LABES- S2S significantly outperforms both supervised- only and semi-supervised baselines. Remark- ably, we can reduce the annotation demands to 50% without performance loss on MultiWOZ. 1 Introduction Belief tracking (also known as dialog state track- ing) is an important component in task-oriented di- alog systems. The system tracks user goals through multiple dialog turns, i.e. infers structured belief states expressed in terms of slots and values (e.g. in Figure 1), to query an external database (Hender- son et al., 2014). Different belief tracking models have been proposed in recent years, either trained independently (Mrkˇsi´c et al., 2017; Ren et al., 2018; ∗Corresponding author. 1Code available at https://github.com/thu-spmi/LABES I need to find a Thai restaurant that's in the south section of the city. There are three restaurants in the south part of town that serve Thai food. Do you have a cuisine preference? belief state DB # match: 3 restaurant-food: Thai ; restaurant-area: south Figure 1: The cues for inferring belief states from user inputs and system responses. The system response re- veals the belief state either directly in the form of word repetition (red), or indirectly in the form of the database query result (green) determined by the belief state. Wu et al., 2019) or within end-to-end (E2E) train- able dialog systems (Wen et al., 2017a,b; Liu and Lane, 2017; Lei et al., 2018; Shu et al., 2019; Liang et al., 2020; Zhang et al., 2020). Existing belief trackers mainly depend on super- vised learning with human annotations of belief states for every user utterance. However, collect- ing these turn-level annotations is labor-intensive and time-consuming, and often requires domain knowledge to identify slots correctly. Building E2E trainable dialog systems, called E2E dialog systems for short, even further magnifies the …

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