Search by item HOME > Access full text > Search by item

JBE, vol. 27, no. 5, pp.685-693, September, 2022

DOI: https://doi.org/10.5909/JBE.2022.27.5.685

Dialogue State Tracking using Circumstance Information to Improve the Accuracy of Task-Oriented Dialogue System in Metaverse

Seungyeon Kim and Junseong Bang

C.A E-mail: hjbang21pp@etri.re.kr

Abstract:

The Metaverse is getting popular due to the demands for digital transformation and non-contact communication platforms. A conversation system which facilitates communication is not widely applied yet in Metaverse. In this work, we present a method that revises primitive dialogue state using circumstance information from Metaverse. The presented model that leverages both dialogue and circumstance information consists of a dialogue state tracking module and a circumstance state tracking module. In the model, a dialogue state is updated with an algorithm which compares a dialogue state and a circumstance state. As a conversation that reaffirms user intent is added, a wrong dialogue state can be revised and the accuracy of a conversation system can be improved.



Keyword: Metaverse, Dialogue State Tracking, Circumstance Information

Reference:
[1] J. Bang, “Application of Artificial Intelligence Technology to Expand Metaverse Services,” The Journal of The Korean Institute of Communication Sciences, Vol.39, No.2, pp.64-73, 2022. https://www. dbpia.co.kr/Journal/articleDetail?nodeId=NODE11032345
[2] N. Mrkšic, D. Ó. Séaghdha, T. H. Wen, B. Thomson, and S. Young, “Neural belief tracker: Data-driven dialogue state tracking,” In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, pp.1777–1788, 2017.doi : https://doi.org/10.18653/v1/P17-1163
[3] E. Hosseini-Asl, B. McCann, C. S. Wu, S. Yavuz, and R. Socher, “A simple language model for task-oriented dialogue,” In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, virtual, 2020. https://proceedings.neurips.cc/paper/2020/file/e946209592563 be0f01c844ab2170f0c-Paper.pdf
[4] P. Budzianowski, T. H. Wen, B. H. Tseng, I. Casanueva, S. Ultes, O. Ramadan, and M. Gasic, “Multiwoz - a largescale multi-domain wizard-of-oz dataset for taskoriented dialogue modelling,” In EMNLP, 2018.doi : https://doi.org/10.18653/v1/D18-1547
[5] M. Eric, R. Goel, S. Paul, A. Sethi, S. Agarwal, S. Gao, A. Kumar, A. Goyal, P. Ku, and D. Hakkani-Tur, “MultiWOZ 2.1: A consolidated multi-domain dialogue dataset with state corrections and state tracking baselines,” In Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France, pp.422–428, 2020.doi : https://doi.org/10.48550/arXiv.1907.01669
[6] X. Zang, A. Rastogi, S. Sunkara, R. Gupta, J. Zhang, and J. Chen, “MultiWOZ 2.2 : A dialogue dataset with additional annotation corrections and state tracking baselines,” In Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, Online, pp.109–117, 2020.doi : https://doi.org/10.18653/v1/2020.nlp4convai-1.13
[7] V. Zhong, C. Xiong, and R. Socher, “Global-locally self-attentive encoder for dialogue state tracking,” In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, pp.1458–1467, 2018.doi : https://doi.org/10.18653/v1/P18-1135
[8] H. Lee, J. Lee, and T. Y. Kim, “Sumbt: Slot-utterance matching for universal and scalable belief tracking,” In ACL, 2019.doi : https://doi.org/10.18653/v1/P19-1546
[9] J. Zhang, K. Hashimoto, C. S. Wu, Y. Wang, P. Yu, R. Socher, and C. Xiong, “Find or classify? dual strategy for slot-value predictions on multi-domain dialog state tracking,” In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, Barcelona, Spain (Online), pp.154–167, 2020.doi : https://doi.org/10.48550/arXiv.1910.03544
[10] S. Gao, A. Sethi, S. Agarwal, T. Chung, and D. Hakkani-Tur, “Dialog state tracking: A neural reading comprehension approach,” In SIGDial, 2019.doi : https://doi.org/10.18653/v1/W19-5932
[11] C. S. Wu, A. Madotto, E. Hosseini-Asl, C. Xiong, R. Socher, and P. Fung, “Transferable multi-domain state generator for task-oriented dialogue systems,” In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp.808–819, 2019.doi : https://doi.org/10.18653/v1/P19-1078
[12] S. Kim, S. Yang, G. Kim, and S. W. Lee, “Efficient dialogue state tracking by selectively overwriting memory,” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, pp.567–582, 2020.doi : https://doi.org/10.18653/v1/2020.acl-main.53
[13] G. Chao and I. Lane, “BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer,” In Proc. Interspeech 2019, pp.1468–1472, 2019.doi : https://doi.org/10.21437/Interspeech.2019-1355
[14] L. Chen, B. Lv, C. Wang, S. Zhu, B. Tan, and K. Yu, “Schema-guided multi-domain dialogue state tracking with graph attention neural networks,” In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.34, pp.7521–7528, 2020.doi : https://doi.org/10.1609/aaai.v34i05.6250
[15] F. Ye, J. Manotumruksa, Q. Zhang, S. Li, and E. Yilmaz, “Slot self-attentive dialogue state tracking,” In Proceedings of the Web Conference 2021, pp.1598-1608, 2021.doi : https://doi.org/10.1145/3442381.3449939
[16] M. Heck, C. V. Niekerk, N. Lubis, C. Geishauser, H. C. Lin, M. Moresi, and M. Gasic, “TripPy: A triple copy strategy for value independent neural dialog state tracking,” In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 1st virtual meeting, pp.35–44, 2020.doi : https://doi.org/10.48550/arXiv.2005.02877
[17] Z. Lin, A. Madotto, G. I. Winata, and P. Fung, “MinTL: Minimalist transfer learning for task-oriented dialogue systems,” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, pp.3391–3405, 2020.doi : https://doi.org/10.18653/v1/2020.emnlp-main.273
[18] C. S. Lee, H. Cheng, and M. Ostendorf, “Dialogue state tracking with a language model using schema-driven prompting,” In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp.4937–4949, 2021.doi : https://doi.org/10.18653/v1/2021.emnlp-main.404
[19] S. Mehri, M. Eric, and D. Hakkani-Tur, “Dialoglue: A natural language understanding benchmark for task-oriented dialogue,” arXiv preprint arXiv:2009.13570, 2020.doi : https://doi.org/10.48550/arXiv.2009.13570
[20] T. Yu, R. Zhang, A. Polozov, C. Meek, and A. H. Awadallah, “Score: Pre-training for context representation in conversational semantic parsing,” In International Conference on Learning Representations, 2021. https://openreview.net/forum?id=oyZxhRI2RiE
[21] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” In NAACL-HLT, 2019.doi : https://doi.org/10.18653/v1/N19-1423
[22] K. Cho, B. V. Merrienboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, 2014.doi : https://doi.org/10.48550/arXiv.1409.1259
[23] X. Tian, L. Huang, Y. Lin, S. Bao, H. He, Y. Yang, H. Wu, F. Wang, and S. Sun, “Amendable generation for dialogue state tracking,” In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, Online, pp.80–92, 2021.doi : https://doi.org/10.18653/v1/2021.nlp4convai-1.8
[24] J. Bang, and S. Ahn, “UX design and evaluation on conversational bot supporting multi-turn and multi-domain dialogues,” In Proceedings of International Conference on Platform Technology and Service, 2022.

Comment


Editorial Office
1108, New building, 22, Teheran-ro 7-gil, Gangnam-gu, Seoul, Korea
Homepage: www.kibme.org TEL: +82-2-568-3556 FAX: +82-2-568-3557
Copyrightⓒ 2012 The Korean Institute of Broadcast and Media Engineers
All Rights Reserved