Abstract
Most methods of emotional dialogue generation focus on how to make the generated replies express the set emotion categories, while ignoring the control over the semantic content of the replies. To this end, in this paper, we propose a emotion- and content-controllable response generation model, ECCRG. ECCRG allows for text-controlled conditions and integration into the decoding process of the language model through a self-attention layer, enabling more precise control over the content of the generated responses. We use a variety of optimization objectives including self-reconfiguration loss and adversarial learning loss to jointly train the model. Experimental results show that ECCRG can embody the set target content in the generated responses, allowing us to achieve controllability on both emotion and textual content.
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Chen, H., Wang, B., Yang, K., Song, Y. (2024). ECCRG: A Emotion- and Content-Controllable Response Generation Model. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_7
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