@inproceedings{lu-etal-2024-edc,
title = "{EDC}: Effective and Efficient Dialog Comprehension For Dialog State Tracking",
author = "Lu, Qifan and
Ramasubramanian, Bhaskar and
Poovendran, Radha",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.232",
pages = "4151--4165",
abstract = "In Task-Oriented Dialog (TOD) systems, Dialog State Tracking (DST) structurally extracts information from user and system utterances, which can be further used for querying databases and forming responses to users. The two major categories of DST methods, sequential and independent methods, face trade-offs between accuracy and efficiency. To resolve this issue, we propose Effective and Efficient Dialog Comprehension (EDC), an alternative DST approach that leverages the tree structure of the dialog state. EDC predicts domains, slot names and slot values of the dialog state step-by-step for better accuracy, and efficiently encodes dialog contexts with causal attention patterns. We evaluate EDC on several popular TOD datasets and EDC is able to achieve state-of-the-art Joint Goal Accuracy (JGA). We also show theoretically and empirically that EDC is more efficient than model designs used by previous works.",
}
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<abstract>In Task-Oriented Dialog (TOD) systems, Dialog State Tracking (DST) structurally extracts information from user and system utterances, which can be further used for querying databases and forming responses to users. The two major categories of DST methods, sequential and independent methods, face trade-offs between accuracy and efficiency. To resolve this issue, we propose Effective and Efficient Dialog Comprehension (EDC), an alternative DST approach that leverages the tree structure of the dialog state. EDC predicts domains, slot names and slot values of the dialog state step-by-step for better accuracy, and efficiently encodes dialog contexts with causal attention patterns. We evaluate EDC on several popular TOD datasets and EDC is able to achieve state-of-the-art Joint Goal Accuracy (JGA). We also show theoretically and empirically that EDC is more efficient than model designs used by previous works.</abstract>
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%0 Conference Proceedings
%T EDC: Effective and Efficient Dialog Comprehension For Dialog State Tracking
%A Lu, Qifan
%A Ramasubramanian, Bhaskar
%A Poovendran, Radha
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F lu-etal-2024-edc
%X In Task-Oriented Dialog (TOD) systems, Dialog State Tracking (DST) structurally extracts information from user and system utterances, which can be further used for querying databases and forming responses to users. The two major categories of DST methods, sequential and independent methods, face trade-offs between accuracy and efficiency. To resolve this issue, we propose Effective and Efficient Dialog Comprehension (EDC), an alternative DST approach that leverages the tree structure of the dialog state. EDC predicts domains, slot names and slot values of the dialog state step-by-step for better accuracy, and efficiently encodes dialog contexts with causal attention patterns. We evaluate EDC on several popular TOD datasets and EDC is able to achieve state-of-the-art Joint Goal Accuracy (JGA). We also show theoretically and empirically that EDC is more efficient than model designs used by previous works.
%U https://aclanthology.org/2024.naacl-long.232
%P 4151-4165
Markdown (Informal)
[EDC: Effective and Efficient Dialog Comprehension For Dialog State Tracking](https://aclanthology.org/2024.naacl-long.232) (Lu et al., NAACL 2024)
ACL
- Qifan Lu, Bhaskar Ramasubramanian, and Radha Poovendran. 2024. EDC: Effective and Efficient Dialog Comprehension For Dialog State Tracking. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4151–4165, Mexico City, Mexico. Association for Computational Linguistics.