Dialog State Tracking: A Neural Reading Comprehension Approach

Shuyang Gao, Abhishek Sethi, Sanchit Agarwal, Tagyoung Chung, Dilek Hakkani-Tur


Abstract
Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question what is the state of the current dialog? after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of 47.33% on the standard test split, exceeding current state-of-the-art by 11.75%**.
Anthology ID:
W19-5932
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Venues:
SIGDIAL | WS
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
264–273
Language:
URL:
https://aclanthology.org/W19-5932
DOI:
10.18653/v1/W19-5932
Bibkey:
Cite (ACL):
Shuyang Gao, Abhishek Sethi, Sanchit Agarwal, Tagyoung Chung, and Dilek Hakkani-Tur. 2019. Dialog State Tracking: A Neural Reading Comprehension Approach. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 264–273, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Dialog State Tracking: A Neural Reading Comprehension Approach (Gao et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5932.pdf
Data
MultiWOZ