Efficient slot correlation learning network for multi-domain dialogue state tracking
Author:
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-023-05217-z.pdf
Reference48 articles.
1. Ni J, Young T, Pandelea V et al (2023) Recent advances in deep learning based dialogue systems: a systematic survey. Artif Intell Rev 56:3055–3155. https://doi.org/10.1007/s10462-022-10248-8
2. Chen H, Liu X, Yin D et al (2017) A survey on dialogue systems: recent advances and new frontiers. SIGKDD Explor Newsl 19(2):25–35. https://doi.org/10.1145/3166054.3166058
3. Lee H, Lee J, Kim TY (2019) SUMBT: slot-utterance matching for universal and scalable belief tracking. In: Proceedings of the 57th annual meeting of the association for computational linguistics. Association for computational linguistics, Florence, Italy, pp 5478–5483, https://doi.org/10.18653/v1/P19-1546
4. Kim S, Yang S, Kim G et al (2020) Efficient dialogue state tracking by selectively overwriting memory. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for computational linguistics, Online, pp 567–582, https://doi.org/10.18653/v1/2020.acl-main.53
5. Mrkšić N, Ó Séaghdha D, Wen TH et al (2017) Neural belief tracker: data-driven dialogue state tracking. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers). Association for computational linguistics, Vancouver, Canada, pp 1777–1788, https://doi.org/10.18653/v1/P17-1163
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