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Computer Science > Computation and Language

arXiv:2109.14144 (cs)
[Submitted on 29 Sep 2021 (v1), last revised 14 Nov 2021 (this version, v2)]

Title:Improving Dialogue State Tracking by Joint Slot Modeling

Authors:Ting-Rui Chiang, Yi-Ting Yeh
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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. Our implementation is available at this https URL.
Comments: Accepted to the 3rd Workshop on NLP for ConvAI in EMNLP 2021
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2109.14144 [cs.CL]
  (or arXiv:2109.14144v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2109.14144

Submission history

From: Ting-Rui Chiang [view email]
[v1] Wed, 29 Sep 2021 02:25:39 UTC (5,618 KB)
[v2] Sun, 14 Nov 2021 18:41:06 UTC (5,616 KB)
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