As a result of the widespread use of intelligent assistants, personalization in dialogue systems has become a hot topic in both research and industry. Typically, training such systems is computationally expensive, especially when using recent large language models. To address this challenge, we develop an approach to personalize dialogue systems using adapter layers and topic modelling. Our implementation enables the model to incorporate user-specific information, achieving promising results by training only a small fraction of parameters.

Braga, M., Raganato, A., Pasi, G. (2023). Personalization in BERT with Adapter Modules and Topic Modelling. In Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023) (pp.24-29). CEUR-WS.

Personalization in BERT with Adapter Modules and Topic Modelling

Braga M.
;
Raganato A.;Pasi G.
2023

Abstract

As a result of the widespread use of intelligent assistants, personalization in dialogue systems has become a hot topic in both research and industry. Typically, training such systems is computationally expensive, especially when using recent large language models. To address this challenge, we develop an approach to personalize dialogue systems using adapter layers and topic modelling. Our implementation enables the model to incorporate user-specific information, achieving promising results by training only a small fraction of parameters.
paper
Adapters; Personalization; Retrieval based chatbot; Topic Modelling;
English
13th Italian Information Retrieval Workshop, IIR 2023 - 8 June 2023through 9 June 2023
2023
Nardini, FM; Tonellotto, N; Faggioli, G; Ferrara, A
Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023)
2023
3448
24
29
https://ceur-ws.org/Vol-3448/
open
Braga, M., Raganato, A., Pasi, G. (2023). Personalization in BERT with Adapter Modules and Topic Modelling. In Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023) (pp.24-29). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/454534
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