Abstract
The rapid development of artificial intelligence (AI) technology has enabled large-scale AI applications to land in the market and practice. However, plenty of security issues have been exposed to society while AI technology has brought many conveniences to humankind, especially for the chatbot with online learning. This paper proposes a speech censorship chatbot system with reinforcement learning, which is mainly composed of two parts: the aggressive speech censorship model and the speech purification model. The aggressive speech censorship can combine the context of user input sentences to detect aggressive speech and respond to the rapid evolution of aggressive speech. According to the situation of the chatbot that is polluted by large numbers of aggressive speech, the speech purification model has the capacity to "forget" the learned malicious data through reinforcement learning rather than rolling back to the early versions. In addition, by integrating few-shot learning, the speed of speech purification is accelerated while reducing the influence on the quality of replies. The experimental results show that our proposed method reduces the probability of generating aggressive speeches and that the integration of the few-shot learning improves the training speed rapidly while effectively slowing down the decline in BLEU values.
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This research is supported by the National Natural Science Foundation of China under Grant 61873160, Grant 61672338, and the Natural Science Foundation of Shanghai under Grant 21ZR1426500.
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Cai, S., Han, D., Li, D. et al. An reinforcement learning-based speech censorship chatbot system. J Supercomput 78, 8751–8773 (2022). https://doi.org/10.1007/s11227-021-04251-z
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DOI: https://doi.org/10.1007/s11227-021-04251-z