방송공학회논문지 (Journal of Broadcast Engineering)

  • 제27권5호
  • /
  • Pages.685-693
  • /
  • 2022
  • /
  • 1226-7953(pISSN)
  • /
  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
권호 표지
DOI QR코드

DOI QR Code

메타버스에서 목적 지향 대화 시스템의 정확도 향상을 위한 상황 정보 활용 대화 상태 추적 기술

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

  • 투고 : 2022.07.16
  • 심사 : 2022.09.13
  • 발행 : 2022.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

디지털 전환과 비대면 소통 플랫폼에 대한 요구로 메타버스가 주목받고 있지만, 원활한 의사소통을 돕는 대화 시스템이 아직 메타버스에서는 널리 적용되지 않았다. 본 연구에서는 메타버스에 대화 시스템을 적용하는 경우 메타버스에서의 주변 상황에 대한 정보를 이용하여 기존의 대화 상태를 수정하는 방법을 제안한다. 대화와 상황에 대한 정보를 모두 활용하는 본 모델은 대화 상태를 추적하는 모듈과 상황 상태를 추적하는 모듈, 그리고 추적한 상황 상태와 대화 상태를 비교하여 수정하는 알고리즘으로 구성된다. 사용자의 의도를 재확인하는 대화가 추가됨에 따라 잘못된 대화 상태를 수정할 수 있고, 대화 시스템의 정확도 향상이 가능하다.

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.

키워드

과제정보

이 논문은 2022년도 정부(경찰청)의 재원을 지원받아 수행된 연구결과임[대화형 치안 지식서비스 폴봇 개발/PR09-01-000-20].

참고문헌

  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 1032345
  2. N. Mrksic, D. O. Seaghdha, 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.