Building a Personalized Dialogue System with Prompt-Tuning (2022)

Abstract: Dialogue systems without consistent responses are not attractive. In this study, we build a dialogue system that can respond based on a given character setting (persona) to bring consistency. Considering the trend of the rapidly increasing scale of language models, we propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models. The results of the automatic and manual evaluations in English and Japanese show that it is possible to build a dialogue system with more natural and personalized responses with less computational resources than fine-tuning.

## introduction large dialogue corpora used to train dialogue systems using neural network models contain utterances from various speakers. this has the disadvantage that the trained system is often inconsistent in the generated utterances @xcite . for example, after the system says, "i am from tokyo," it might say, "i am from kyoto." we aim to build a dialogue system that can respond based on a persona to avoid inconsistent utterances. a simple method of giving a persona to a model can be to concatenate the persona to the model's input in natural language @xcite . however, this method is not suitable because the more persona information is added, the longer the input text becomes. therefore, we propose to freeze all parameters of a pre-trained language model and add a new fixed-length prompt before the input token sequence to embed the persona information. specifically, only the embedding vectors of the added prompt are optimized using a dialogue corpus in which utterances are made based on the persona. we conduct experiments on two languages: english and japanese. automatic and manual evaluations show that our method can build a dialogue system capable of natural responses based on a persona. since our approach does not update the parameters of the pre-trained model, it can reduce the computational cost required for training. we also show that it is possible to build a personalized dialogue system with even a small dataset consisting of hundreds to thousands of utterance-response pairs. ## method this section describes our proposed method. the detailed setup for our experiments is described in sections 4.1 and 4.2. ## experiments based on the method in section 3, we build a personalized dialogue system. we used hugging face's transformers to build the system and the nvidia a100 sxm4 gpu with a gpu memory size of 40 gb. the main experiments are conducted in english, and the results of additional experiments in japanese are included at the end of this section. ## conclusion we proposed a method for prompt-tuning a pretrained language model using dialogue data based on a single persona. automatic and manual evaluations showed that we could construct a dialogue system that can respond more naturally and personabased, with less computational resources than finetuning. compared to the generated responses in english, those in japanese look natural due to the