Wei Guan
ABSTRACT
With the continuous progress of neural network technology in different fields, people have put forward higher requirements and prospects for the research of deep neural network technology in the field of artificial intelligence, and language intelligence is the core problem of artificial intelligence. As an important research content in the field of natural language processing, dialogue generation has been widely concerned by the academic community for a long time, and this research is also widely used in people's daily lives, such as medical Q&A, e-commerce shopping, emotional response, etc. There are still many problems in the existing model for dialogue generation research, such as easy to produce universal replies ("ok", "emm", etc.), the emotional information of the reply statement is not strong enough, and the response is not related to the subject. For this problem, we propose to use an emotional dialogue generation model based on a generative adversarial network to generate statements with emotional responses, and our network has a generator and multi-level discriminator. In terms of discriminators, multi-level discourse discriminators are introduced to guide the generation of replies and the strengthening of emotional information, to discriminate emotions at the individual word level and sentence level, to solve the problem that the same text may be different in different semantic environments and the emotional information of some words is not clear in some cases, in order to achieve maximum accuracy the emotional discrimination results are fed back to the generator and guide the generation of generator statements. In addition, compared with the traditional generative adversarial network, the task processing ability for large pieces of text is improved, and the model has been shown to produce emotional reply statements with better baseline levels than in the past.
- Mondal, B. Artificial intelligence: state of the art. Recent Trends and Advances in Artificial Intelligence and Internet of Things 2020, 389-425.Google Scholar
- Goralski, M.A.; Tan, T.K. Artificial intelligence and sustainable development. The International Journal of Management Education 2020, 18, 100330.Google Scholar
- Huang, M.; Zhu, X.; Gao, J. Challenges in building intelligent open-domain dialog systems. ACM Transactions on Information Systems (TOIS) 2020, 38, 1-32.Google Scholar
- Shao, L.; Gouws, S.; Britz, D.; Goldie, A.; Strope, B.; Kurzweil, R. Generating long and diverse responses with neural conversation models. 2016.Google Scholar
- Yu, L.; Zhang, W.; Wang, J.; Yu, Y. Seqgan: Sequence generative adversarial nets with policy gradient. In Proceedings of the Proceedings of the AAAI conference on artificial intelligence, 2017.Google ScholarCross Ref
- Wang, K.; Wan, X. SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks. In Proceedings of the IJCAI, 2018; pp. 4446-4452.Google Scholar
- Wang, K.; Gou, C.; Duan, Y.; Lin, Y.; Zheng, X.; Wang, F.-Y. Generative adversarial networks: introduction and outlook. IEEE/CAA Journal of Automatica Sinica 2017, 4, 588-598.Google Scholar
- Seo, J.; Yoon, T.; Kim, J.; Yow, K.C. One-to-one Example-based Automatic Image Coloring Using Deep Convolutional Generative Adversarial Network. Journal of Advances in Information Technology Vol 2017, 8.Google Scholar
- Guo, J.; Lu, S.; Cai, H.; Zhang, W.; Yu, Y.; Wang, J. Long text generation via adversarial training with leaked information. In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, 2018.Google ScholarCross Ref
- Nie, W.; Narodytska, N.; Patel, A. Relgan: Relational generative adversarial networks for text generation. In Proceedings of the International conference on learning representations, 2018.Google Scholar
- Fedus, W.; Goodfellow, I.; Dai, A.M. Maskgan: better text generation via filling in the_. arXiv preprint arXiv:1801.07736 2018.Google Scholar
- Salovey, P.; Mayer, J.D. Emotional intelligence. Imagination, cognition and personality 1990, 9, 185-211.Google Scholar
- Guo, D. Tracking student sentiment from social media. Journal of Advances in Information Technology 2015, 6.Google Scholar
- Darshan, H.; Shankar, A.R.; Harish, B.; HM, K.K. Exploiting RLPI for sentiment analysis on movie reviews. Journal of Advances in Information Technology Vol 2019, 10.Google Scholar
- Zhao, X.; Ohsawa, Y. Sentiment analysis on the online reviews based on hidden Markov model. Journal of Advances in Information Technology 2018, 9.Google Scholar
- Darwich, M.; Noah, S.A.M.; Omar, N.; Osman, N.A.; Said, I. Quantifying the natural sentiment strength of polar term senses using semantic gloss information and degree adverbs. Journal of Advances in Information Technology Vol 2020, 11.Google Scholar
- Hamouda, A.; Marei, M.; Rohaim, M. Building machine learning based senti-word lexicon for sentiment analysis. Journal of advances in information technology 2011, 2, 199-203.Google Scholar
- Velampalli, S.; Muniyappa, C.; Saxena, A. Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models. Journal of Advances in Information Technology 2022, 13,Google Scholar
- Zhou, H.; Huang, M.; Zhang, T.; Zhu, X.; Liu, B. Emotional chatting machine: Emotional conversation generation with internal and external memory. In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, 2018.Google ScholarCross Ref
Index Terms
- Research on the Generation of Emotional Dialogue Statements in Generative Adversarial Networks
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