Implementation and Evaluation of Interviewer’s Response Generation Model Considering Semantic Content

Author:

Zeng Jie1,Nakano Yukiko1,Sakato Tatsuya1

Affiliation:

1. Faculty of Science and Technology, Seikei University

Publisher

Japanese Society for Artificial Intelligence

Reference35 articles.

1. [Bobrow 77] Bobrow,D.G., Kaplan,R.M., Kay,M., Norman,D.A., Thompson,H., and Winograd,T.: GUS, a frame-driven dialog system, Artificial Intelligence, Vol.8, No.2, pp. 155-173 (1977)

2. [Brown 20] Brown,T., Mann,B., Ryder,N., Subbiah,M., Kaplan,J.D., Dhariwal,P., Neelakantan,A., Shyam,P., Sastry,G., Askell,A., etal.: Language models are few-shot learners, Advances in Neural Information Processing Systems, Vol.33, pp. 1877-1901 (2020)

3. [Bunt 12] Bunt,H., Alexandersson,J., Choe,J.-W., Fang,A.C., Hasida,K., Petukhova,V., Popescu-Belis,A., and Traum,D.: ISO 24617-2: A semantically-based standard for dialogue annotation, in Proceedings of the 8th International Conference on Language Resources and Evaluation, pp. 430-437 (2012)

4. [Chen 19] Chen,W., Chen,J., Qin,P., Yan,X., and Wang,W.Y.: Semantically conditioned dialog response generation via hierarchical disentangled self-attention, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3696-3709 (2019)

5. [Galetzka 21] Galetzka,F., Rose,J., Schlangen,D., and Lehmann,J.: Space efficient context encoding for non-task-oriented dialogue generation with graph attention transformer, in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7028-7041 (2021)

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