Abstract
This paper describes the training process of neural network that can extract additional knowledge from the Internet and long-term memory with the goal to improve the quality of generated dialogue responses. Modern language models, due to their large size and high-quality data, can generate meaningful texts, including dialogues with other speakers. Meanwhile, their knowledge is frozen in time by the data they were trained on. Without re-training, such models are not able to acquire new relevant knowledge. We propose one of the possible solutions to this problem, in which the neural network model will be able to use the knowledge received from the Internet and long-term memory to generate dialogue responses. Using these methods we improved BLEU-1 metric by 43% and BLEU-2 metric by 45% on Toloka Persona Chat Rus dataset.
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Acknowledgment
The research was financially supported by the Russian Science Foundations (project 22-11-00128).
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Apanasovich, K., Makhnytkina, O., Matveev, Y. (2023). Development and Research of Dialogue Agents with Long-Term Memory and Web Search. In: Karpov, A., Samudravijaya, K., Deepak, K.T., Hegde, R.M., Agrawal, S.S., Prasanna, S.R.M. (eds) Speech and Computer. SPECOM 2023. Lecture Notes in Computer Science(), vol 14338. Springer, Cham. https://doi.org/10.1007/978-3-031-48309-7_32
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