Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking (2022)

Abstract: In dialogue state tracking, dialogue history is a crucial material, and its utilization varies between different models. However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated. Apparently, it requires different dialogue history to update different slots in different turns. Therefore, using consistent dialogue contents may lead to insufficient or redundant information for different slots, which affects the overall performance. To address this problem, we devise DiCoS-DST to dynamically select the relevant dialogue contents corresponding to each slot for state updating. Specifically, it first retrieves turn-level utterances of dialogue history and evaluates their relevance to the slot from a combination of three perspectives: (1) its explicit connection to the slot name; (2) its relevance to the current turn dialogue; (3) Implicit Mention Oriented Reasoning. Then these perspectives are combined to yield a decision, and only the selected dialogue contents are fed into State Generator, which explicitly minimizes the distracting information passed to the downstream state prediction. Experimental results show that our approach achieves new state-of-the-art performance on MultiWOZ 2.1 and MultiWOZ 2.2, and achieves superior performance on multiple mainstream benchmark datasets (including Sim-M, Sim-R, and DSTC2).

## introduction task-oriented dialogue systems have recently attracted growing attention and achieved substantial progress. dialogue state tracking (dst) is a core component, where it is responsible for interpreting user goals and intents and feeding hotel-type: @xcite hotel-pricerange: @xcite hotel-type: @xcite hotel-name: ['worth house'] hotel-bookday: ['monday'] hotel-bookpeople: @xcite ... downstream policy learning in dialogue management. the common practice treats it as a problem of compacting the dialogue content into a series of slot-value pairs that represent information about the user goals updated until the current turn. for example, in figure 1 , the dialogue state at turn 2 is {("hotel -type", "guesthouse"), ("hotel -pricerange", "cheap")}. ## related work there has been a plethora of research on dialogue state tracking. traditional dialogue state trackers relied on a separate spoken language understanding (slu) module @xcite to extract relevant information. in recent years, neural network models are proposed for further improvements. one way to classify dst models is whether they use dialogue pre-trained language model pre-trained language model encoder [cls]t bt-1 [sep] dt [sep] [cls]t bt-1 [sep] dt [sep] state update predictor @xmath0... ... ... ... ... ## approach the architecture of dicos-dst is illustrated in figure 2 . dicos-dst consists of encoder, state update predictor, multi-perspective dialogue collaborative selector, and state generator. here we first define the problem setting in our work. we define the number of the current turn as t . the task is to predict the dialogue state at each turn t (@xmath1, which is defined as b @xmath2, v j t )|1 ≤ @xmath3, where s j is the slot name, v j t is the corresponding slot value, and j is the total number of slots. for the sake of simplicity, we omit the superscript t in the variables in the next sections. ## conclusion we introduce an effective dicos-dst that dynamically selects the relevant dialogue contents corresponding to each slot from a combination of three perspectives. the dialogue collaborative selector module performs a comprehensive selection for each turn dialogue based on its relation to the slot name, its connection to the current turn dialogue, and the implicit mention oriented reasoning. then only the selected dialogue contents are fed into state generator, which explicitly minimizes the distracting information passed to the downstream state prediction. our dicos-dst model achieves new state-of-the-art performance on the multiwoz benchmark, and achieves competitive performance on most other dst benchmark datasets. the potential relationship among the above perspectives is a promising research direction, and we will explore it for more than dialogue selection in the future. b statistics of datasets in experiments characteristics multiwoz 2.1 multiwoz 2.2 sim-m & sim-r dstc2 no. of domains 7 8 2 1 no. of dialogues 8,438 8,438 1,500 1612 total no. of turns 113,556 113,556 14,796 23354 avg. turns per dialogue 13.46 13.46 9.86 14.49 avg. tokens per turn 13.38 13.13 8.24 8.54 no. of categorical slots 37 21 0 3 no. of non-categorical slots 0 40 14 0 have schema description yes yes no no