PAR : Persona Aware Response in Conversational Systems (2022)

Abstract: To make the Human Computer Interaction more user friendly and persona aligned, detection of user persona is of utmost significance. Towards achieving this objective, we describe a novel approach to select the persona of a user from pre-determine list of personas and utilize it to generate personalized responses. This is achieved in two steps. Firstly, closest matching persona is detected from a set of pre-determined persona for the user. The second step involves the use of a fine-tuned natural language generation (NLG) model to generate persona compliant responses. Through experiments, we demonstrate that the proposed architecture generates better responses than current approaches by using a detected persona. Experimental evaluation on the PersonaChat dataset has demonstrated notable performance in terms of perplexity and F1-score.

## introduction since the dawn of deep learning era and advancements in natural language processing (nlp), researchers have tried to make the human-computer interaction (hci) more natural and smooth @xcite . several features are incorporated in a conversation based system to induce humanlike response and give users a pleasant experience such as -sentiment analysis of user replies @xcite , emotion recognition @xcite etc. however, there is still a gap left between the desired output and the approaches tried. a user's conversation with a bot highly depends on the general nature and likings of the user, more commonly referred to as the persona. many recent studies have explored persona identification from user utterances @xcite @xcite . a dataset of persona enhanced chat between two users based on a given persona profile, known as personachat was introduced in @xcite . experiments performed using several generative and ranking baseline models established the need of including persona information to enhance conversations. the study in @xcite have proposed a modified dataset pmpc on personachat dataset and explored utterance-to-profile matching network for the task of many-to-many matching. moreover, in @xcite , a trasmitter-reciever architecture is proposed, referred to as p 2 bot which models the mutual persona perception using a transformer with reinforcement learning to fine tune the conversations during training. also, to alleviate the challenges associated with data in persona based dialog generation, the study in @xcite introduced data manipulation techniques such as distillation, diversification and curriculum. the architecture considered were baseline gpt2 and transormer encoder-decoder stack. despite several attempts by researchers, the generation of persona complaint dialogue is very challenging. firstly, automated metrics such as bleu, rogue etc does not consider semantic similarity and will present poor scores if there is no overlap between the generated response and the gold response. also, the response generated can be persona complaint but may not answer the query of the user. as such, we propose a novel approach to tackle the persona based dialog generation problem. firstly, we select the matched persona corresponding to the user input from the set of pre-determined personas of the user, by using similarity matching of the embeddings. then, we generate persona complaint response by incorporating this additional persona information with state-of-the-art semantically controlled generative pre training (sc-gpt)(peng et al., 2020 the rest of the paper is organized as follows. section 2 describes the methodology for persona classification and response generation. section 3 details the experiments performed with dataset information and hypermaters used. section 4 presents the results along with sample generated responses. section 5 concludes the paper. ## methodology this section describes the methodology used for persona selection, given a user utterance and predetermined list of persona and subsequent natural language generation (nlg) module which is responsible for the generation of persona complaint bot response. ## experimental evaluation in this section, we discuss the dataset details along with hyper-parameter tuning for the sc-gpt model for persona-based dialog generation. ## conclusion we have proposed a methodology which first identifies the most relevant persona corresponding to a user utterance, from a list of given personas. then persona complaint response is generated based on the selected persona. we utilized cosine similarity based classification of sentence embeddings to select the relevant persona. sc-gpt is used to finetune on subsets of personachat dataset for the task of response generation. experimental evaluation is performed on the validation partition of the per-sonachat dataset and perplexity score as well as f1 score is reported. we have also reported sample responses generated by the model, which shows that the proposed methodology is efficient in generating persona complaint responses.