@inproceedings{chiang-yeh-2021-improving,
title = "Improving Dialogue State Tracking by Joint Slot Modeling",
author = "Chiang, Ting-Rui and
Yeh, Yi-Ting",
editor = "Papangelis, Alexandros and
Budzianowski, Pawe{\l} and
Liu, Bing and
Nouri, Elnaz and
Rastogi, Abhinav and
Chen, Yun-Nung",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4convai-1.15",
doi = "10.18653/v1/2021.nlp4convai-1.15",
pages = "155--162",
abstract = "Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types that share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoz 2.1 from 58.7 to 61.3.",
}
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<abstract>Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types that share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoz 2.1 from 58.7 to 61.3.</abstract>
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%0 Conference Proceedings
%T Improving Dialogue State Tracking by Joint Slot Modeling
%A Chiang, Ting-Rui
%A Yeh, Yi-Ting
%Y Papangelis, Alexandros
%Y Budzianowski, Paweł
%Y Liu, Bing
%Y Nouri, Elnaz
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F chiang-yeh-2021-improving
%X Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types that share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoz 2.1 from 58.7 to 61.3.
%R 10.18653/v1/2021.nlp4convai-1.15
%U https://aclanthology.org/2021.nlp4convai-1.15
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.15
%P 155-162
Markdown (Informal)
[Improving Dialogue State Tracking by Joint Slot Modeling](https://aclanthology.org/2021.nlp4convai-1.15) (Chiang & Yeh, NLP4ConvAI 2021)
ACL