Tracking Must Go On: Dialogue State Tracking with Verified Self-Training
- Title
- Tracking Must Go On: Dialogue State Tracking with Verified Self-Training
- Authors
- LEE, GARY GEUNBAE; Lee, Jihyun; Lee, Chaebin; Kim, Yunsu
- Date Issued
- 2023-08-20
- Publisher
- International Speech Communication Association
- Abstract
- In task-oriented dialogues, dialogue state tracking (DST) is a critical component as it identifies specific information for the user's purpose. However, as annotating DST data requires a significant amount of human effort, leveraging raw dialogue is crucial. To address this, we propose a new self-training (ST) framework with a verification model. Unlike previous ST methods that rely on extensive hyper-parameter searching to filter out inaccurate data, our verification methodology ensures the accuracy and validity of the dataset without using a fixed threshold. Furthermore, to mitigate overfitting, we augment the dataset by generating diverse user utterances. Even when using only 10% of the labeled data, our approach achieves comparable results to a fully labeled MultiWOZ2.0 dataset. The evaluation of scalability also demonstrates enhanced robustness in predicting unseen values.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/121282
- Article Type
- Conference
- Citation
- 24th International Speech Communication Association, Interspeech 2023, page. 4678 - 4682, 2023-08-20
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- There are no files associated with this item.
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