The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Neural Belief Tracker: Data-Driven Dialogue State Tracking
Open accessAbstract
One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue.However, most current approaches have difficulty scaling to larger, more complex dialogue domains.This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users' language.We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning.NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context.Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.
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Cites
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Self-citationAttention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
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Self-citationThe Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
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MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Ultes, S., Ramadan, O., & Gašić, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1547 · Cited by 1,028
Self-citationA Survey on Dialogue Systems: Recent Advances and New Frontiers
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The Natural Language Decathlon: Multitask Learning as Question Answering
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When large language models meet personalization: perspectives of challenges and opportunities
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., et al. (2024) · World Wide Web · doi:10.1007/s11280-024-01276-1 · Cited by 367
“…dialogue systems, a common approach [24, 204] is to treat the response generation as a pipeline and handle it separately using four components: dialogue understanding [205, 206], dialogue state tracking [207–209], dialogue policy learning [24, 210], and natural language generation [211, 212].”
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Metadata
- Authors
- Mrkšić, N., Ó Séaghdha, D., Wen, T.-H., Thomson, B., & Young, S.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1777-1788
- OpenAlex
- W2432549722
- Cited by
- 459
- References
- 37
- FWCI
- 28.20
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Mrkšić, N., Ó Séaghdha, D., Wen, T.-H., Thomson, B., & Young, S. (2017). Neural Belief Tracker: Data-Driven Dialogue State Tracking. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1163
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