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2021, 3(1): 18-32 Published Date:2021-2-20

DOI: 10.1016/j.vrih.2020.12.001

Emotional dialog generation via multiple classifiers based on a generative adversarial network

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Abstract:

Background
Human-machine dialog generation is an essential topic of research in the field of natural language processing. Generating high-quality, diverse, fluent, and emotional conversation is a challenging task. Based on continuing advancements in artificial intelligence and deep learning, new methods have come to the forefront in recent times. In particular, the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate responses. However, neural network models come with their own set of questions and challenges. The basic conversational model framework tends to produce universal, meaningless, and relatively "safe" answers.
Methods
Based on generative adversarial networks (GANs), a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog generation. The proposed model comprises a generative and three discriminative models. The generator is based on the basic sequence-to-sequence (Seq2Seq) dialog generation model, and the aggregate discriminative model for the overall framework consists of a basic discriminative model, an emotion discriminative model, and a fluency discriminative model. The basic discriminative model distinguishes generated fake sentences from real sentences in the training corpus. The emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion, and directs the generative model to generate dialogs that correspond to the category of the pre-specified emotion. Finally, the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent sentences.
Results
Based on the experimental results, this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy, fluency, and consistency.
Conclusions
The proposed EMC-GAN model is capable of generating consistent, smooth, and fluent dialog that conveys pre-specified emotions, and exhibits better performance with respect to emotional accuracy, consistency, and fluency compared to its competitors.
Keywords: Emotional dialog generation ; Sequence-to-sequence model ; Emotion classification ; Generative adversarial networks ; Multiple classifiers

Cite this article:

Wei CHEN, Xinmiao CHEN, Xiao SUN. Emotional dialog generation via multiple classifiers based on a generative adversarial network. Virtual Reality & Intelligent Hardware, 2021, 3(1): 18-32 DOI:10.1016/j.vrih.2020.12.001

1. Young T, Cambria E, Chaturvedi I, Zhou H, Biswas S, Huang M. Augmenting end-to-end dialogue systems with commonsense knowledge. Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans, Louisiana, USA, 2018, 4970–4977

2. Young T, Pandelea V, Poria S, Cambria E. Dialogue systems with audio context. Neurocomputing, 2020, 388: 102–109 DOI:10.1016/j.neucom.2019.12.126

3. Xu H T, Peng H Y, Xie H R, Cambria E, Zhou L Y, Zheng W G. End-to-End latent-variable task-oriented dialogue system with exact log-likelihood optimization. World Wide Web, 2020, 23(3): 1989–2002 DOI:10.1007/s11280-019-00688-8

4. Zhang Z, Liao L Z, Huang M L, Zhu X Y, Chua T S. Neural multimodal belief tracker with adaptive attention for dialogue systems. In: The World Wide Web Conference on-WWW'19. San Francisco, CA, USA, ACM Press, 2019, 2401–2412 DOI:10.1145/3308558.3313598

5. Ma Y K, Nguyen K L, Xing F Z, Cambria E. A survey on empathetic dialogue systems. Information Fusion, 2020 DOI:10.1016/j.inffus.2020.06.011

6. Paschke A, Boley H. Rule responder: rule-based agents for the semantic-pragmatic web. International Journal on Artificial Intelligence Tools, 2011, 20(6): 1043–1081 DOI:10.1142/s0218213011000528

7. Xu M H, Li P J, Yang H R, Ren P J, Ren Z C, Chen Z M, Ma J. A neural topical expansion framework for unstructured persona-oriented dialogue generation. 2020

8. Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar, Association for Computational Linguistics, 2014, 1724–1734 DOI:10.3115/v1/d14-1179

9. Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. 2014

10. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. 2014

11. Vinyals O, Le Q. A neural conversational model. 2015

12. Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial networks. 2014

13. Yu L, Zhang W, Wang J, Yu Y. SeqGAN: sequence generative adversarial nets with policy gradient. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, California, AAAI, 2017, 2852-2858

14. Kaelbling L P, Littman M L, Moore A W. Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 1996, 4(1): 237–285 DOI:10.1613/jair.301

15. Sutton R S, McAllester D A, Singh S P, Mansour Y. Policy gradient methods for reinforcement learning with function approximation. In: Advances in Neural Information Processing Systems 12, NIPS Conference. Denver, Colorado, USA, 1999, 1057–1063

16. Li J W, Monroe W, Shi T L, Jean S, Ritter A, Jurafsky D. Adversarial learning for neural dialogue generation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark, Association for Computational Linguistics, 2017, 2157–2169 DOI:10.18653/v1/d17-1230

17. Cui S B, Lian R Z, Jiang D, Song Y F, Bao S Q, Jiang Y. DAL: dual adversarial learning for dialogue generation. 2019

18. Salovey P, Mayer J D. Emotional intelligence. Imagination, Cognition and Personality, 1990, 9(3): 185–211 DOI:10.2190/dugg-p24e-52wk-6cdg

19. Ghosh S, Chollet M, Laksana E, Morency L P, Scherer S. Affect-LM: a neural language model for customizable affective text generation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). PA, USA, Association for Computational Linguistics, 2017, 634–642 DOI:10.18653/v1/p17-1059

20. Rashkin H, Smith E M, Li M, Boureau Y L. Towards empathetic open-domain conversation models: a new benchmark and dataset. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy, Association for Computational Linguistics, 2019 DOI:10.18653/v1/p19-1534

21. Zhou H, Huang M L, Zhang T Y, Zhu X Y, Liu B. Emotional chatting machine: emotional conversation generation with internal and external memory. 2017

22. Wang K, Wan X. SentiGAN: Generating sentimental texts via mixture adversarial networks. In: Twenty-Seventh International Joint Conforence on Artificial Intelligence. Stockholm, Sweden, 2018, 4446-4452 DOI:10.24963/ijcai.2018/618

23. Sun X, Peng X Q, Ding S. Emotional human-machine conversation generation based on long short-term memory. Cognitive Computation, 2018, 10(3): 389–397 DOI:10.1007/s12559-017-9539-4

24. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. 2014

25. Graves A, Mohamed A R, Hinton G. Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, BC, Canada, IEEE, 2013, 6645–6649 DOI:10.1109/icassp.2013.6638947

26. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X. Improved techniques for training GANs. Advances In Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016. Barcelona, Spain, 2016, 2226–2234

27. Lai S, Xu L, Liu K, Zhao, J. Recurrent convolutional neural networks for text classification. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin, Texas, AAAI, 2015, 2267–2273

28. Srivastava R K, Greff K, Schmidhuber J. Highway networks. 2015

29. Zhang X, LeCun Y. Text Understanding from Scratch. 2015

30. Liu D. Approaches to Chinese word analysis; utterance segmentation and automatic evaluation of machine translation. Beijing: Chinese Academy of Sciences, 2004

31. Doddington G. Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In: Proceedings of the second international conference on Human Language Technology Research-. San Diego, California, Morristown, NJ, USA, Association for Computational Linguistics, 2002, 138–145 DOI:10.3115/1289189.1289273

32. Zhang H P, Yu H K, Xiong D Y, Liu Q. HHMM-based Chinese lexical analyzer ICTCLAS. In: Proceedings of the second SIGHAN workshop on Chinese language processing. NJ, USA, Association for Computational Linguistics, 2003 DOI:10.3115/1119250.1119280

33. Sun X, Chen X M, Pei Z M, Ren F J. Emotional human machine conversation generation based on SeqGAN. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia). Beijing, China, IEEE, 2018, 1–6 DOI:10.1109/aciiasia.2018.8470388

34. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Derek G. Tensorflow: A system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016). GA, USA, 2016, 265–283

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