Devang Agrawal
2020
Conversational Semantic Parsing for Dialog State Tracking
Jianpeng Cheng
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Devang Agrawal
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Héctor Martínez Alonso
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Shruti Bhargava
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Joris Driesen
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Federico Flego
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Dain Kaplan
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Dimitri Kartsaklis
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Lin Li
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Dhivya Piraviperumal
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Jason D. Williams
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Hong Yu
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Diarmuid Ó Séaghdha
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Anders Johannsen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
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.
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