Controlled Neural Response Generation by Given Dialogue Acts Based on Label-aware Adversarial Learning

Author:

Kawano Seiya1,Yoshino Koichiro2,Nakamura Satoshi1

Affiliation:

1. Nara Institute of Science and Technology

2. Guardian Robot Project (GRP)

Publisher

Japanese Society for Artificial Intelligence

Subject

Artificial Intelligence,Software

Reference48 articles.

1. [Alston 00] Alston, W. P.:Illocutionary Acts and Sentence Meaning,Cornell University Press (2000)

2. [Boyer 10] Boyer, K., Ha, E. Y., Phillips, R., Wallis, M., Vouk, M.,and Lester, J.: Dialogue act modeling in a complex task-oriented domain, in Proceedings of the SIGDIAL 2010 Conference, pp. 297–305 (2010)

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

4. [Cho 14] Cho, K., Merrienboer, van B., Gulcehre, C., Bahdanau, D.,Bougares, F., Schwenk, H., and Bengio, Y.: Learning phrase representations using RNN encoder–decoder for statistical machine translation, in Proceedings of EMNLP, pp. 1724–1734 (2014)

5. [Devlin 19] Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.:BERT: Pre-training of deep bidirectional transformers for language understanding, in Proceedings of the 2019 Conference of the NorthAmerican Chapter of the Association for Computational Linguistics:Human Language Technologies, Volume 1 (Long and Short Papers),pp. 4171–4186 (2019)

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