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On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools

2022-02-15 · Zhoujian Sun, Zhengxing Huang, Nai Ding

Dialogue state tracking (DST) is a component of the task-oriented dialogue system. It is responsible for extracting and managing slot values according to dialogue utterances, where each slot represents an essential part of the information to accomplish a task, and slot value is updated recurrently in each dialogue turn. However, many DST models cannot update slot values appropriately. These models may repeatedly inherit wrong slot values extracted in previous turns, resulting in the fail of the entire DST task. They cannot update indirectly mentioned slots well, either. This study designed a model with a mentioned slot pool (MSP) to tackle the update problem. The MSP is a slot-specific memory that records all mentioned slot values that may be inherited, and our model updates slot values according to the MSP and the dialogue context. Our model rejects inheriting the previous slot value when it predicates the value is wrong. Then, it re-extracts the slot value from the current dialogue context. As the contextual information accumulates with the dialogue progress, the new value is more likely to be correct. It also can track the indirectly mentioned slot by picking a value from the MSP. Experimental results showed our model reached state-of-the-art DST performance on MultiWOZ 2.1 and 2.2 datasets.

📄 PDF Abstract BibTeX arXiv:2202.07156

Code (1)

zjlab-ammi/msp 공식 구현 pytorch

Tasks

Dialogue State Tracking

Methods 이 논문이 사용한 방법론

DST Dynamic sparse training methods train neural networks in a sparse manner, starting with an initial sparse mask, and periodically updating the mask based on some criteria.

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