@inproceedings{cheng-etal-2020-conversational,
title = "Conversational Semantic Parsing for Dialog State Tracking",
author = "Cheng, Jianpeng and
Agrawal, Devang and
Mart{\'\i}nez Alonso, H{\'e}ctor and
Bhargava, Shruti and
Driesen, Joris and
Flego, Federico and
Kaplan, Dain and
Kartsaklis, Dimitri and
Li, Lin and
Piraviperumal, Dhivya and
Williams, Jason D. and
Yu, Hong and
{\'O} S{\'e}aghdha, Diarmuid and
Johannsen, Anders",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.651",
doi = "10.18653/v1/2020.emnlp-main.651",
pages = "8107--8117",
abstract = "We consider a new perspective on dialog state tracking (DST), the task of estimating a user{'}s goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts. We describe an encoder-decoder framework for DST with hierarchical representations, which leads to {\textasciitilde}20{\%} improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs.",
}
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<abstract>We consider a new perspective on dialog state tracking (DST), the task of estimating a user’s goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts. We describe an encoder-decoder framework for DST with hierarchical representations, which leads to ~20% improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs.</abstract>
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%0 Conference Proceedings
%T Conversational Semantic Parsing for Dialog State Tracking
%A Cheng, Jianpeng
%A Agrawal, Devang
%A Martínez Alonso, Héctor
%A Bhargava, Shruti
%A Driesen, Joris
%A Flego, Federico
%A Kaplan, Dain
%A Kartsaklis, Dimitri
%A Li, Lin
%A Piraviperumal, Dhivya
%A Williams, Jason D.
%A Yu, Hong
%A Ó Séaghdha, Diarmuid
%A Johannsen, Anders
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F cheng-etal-2020-conversational
%X We consider a new perspective on dialog state tracking (DST), the task of estimating a user’s goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts. We describe an encoder-decoder framework for DST with hierarchical representations, which leads to ~20% improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs.
%R 10.18653/v1/2020.emnlp-main.651
%U https://aclanthology.org/2020.emnlp-main.651
%U https://doi.org/10.18653/v1/2020.emnlp-main.651
%P 8107-8117
Markdown (Informal)
[Conversational Semantic Parsing for Dialog State Tracking](https://aclanthology.org/2020.emnlp-main.651) (Cheng et al., EMNLP 2020)
ACL
- Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, and Anders Johannsen. 2020. Conversational Semantic Parsing for Dialog State Tracking. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8107–8117, Online. Association for Computational Linguistics.