LUNA : Learning Slot-Turn Alignment for Dialogue State Tracking (2022)

Abstract: Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history. Existing methods generally exploit the utterances of all dialogue turns to assign value for each slot. This could lead to suboptimal results due to the information introduced from irrelevant utterances in the dialogue history, which may be useless and can even cause confusion. To address this problem, we propose LUNA, a SLot-TUrN Alignment enhanced approach. It first explicitly aligns each slot with its most relevant utterance, then further predicts the corresponding value based on this aligned utterance instead of all dialogue utterances. Furthermore, we design a slot ranking auxiliary task to learn the temporal correlation among slots which could facilitate the alignment. Comprehensive experiments are conducted on three multi-domain task-oriented dialogue datasets, MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art results on these datasets.

## introduction dialogue state tracking (dst) refers to the task of estimating the dialogue state (i.e., user's intents) at every dialogue turn, where the state is represented in forms of a set of slot-value pairs @xcite . dst is crucial to the success of a task-oriented dialogue system as the dialogue policy relies on the estimated dialogue state to choose actions. traditional dst approaches assume that all candidate slot-value pairs are predefined in an ontology @xcite @xcite . then, they scores all possible pairs and selecting the value with the highest score as the predicted value of a slot. ## methodologies suppose that there is a conversation composed of t utterances, @xmath0, r 1 ), ..., (q t , r t )}, and a predefined slot set @xmath1, ..., s j }, where q t denotes the user query at t-th utterance, r t is the corresponding system response and j is the total number of slots. dst aims to predict states at each turn with given utterances up-to-now (@xmath2, @xmath3, and presents them as slot-value pairs, b @xmath4, v t 1 ), ..., (s j , v t j )}, where s j is the j-th slot in s , and v t j is the value with respect to s j for the t-th turn. since the datasets are collected from multi domains, following previous works @xcite , we concatenate domain names and slot names as domain specific slots. to tackle this task, we propose the luna model. as depicted in figure 1 , this model consists of three encoders and an alignment network. in this section, we will elaborate each module of this model. ## conclusion in this work, we reveal the problem in dst that exploiting all dialogue utterances to assign value to slots may cause suboptimal results. to alleviate it, we propose luna, a slot-turn alignment enhanced approach. and design a ranking-based auxiliary task to supervise luna to learn the temporal correlations among slots. comprehensive experiments are conducted on multiwoz 2.0, 2.1, and 2.2 and the results show that luna achieves new state-of-the-art results. moreover, the visualization demonstrates the interpretability of luna.