MRE : Multi Relationship Extractor for Persona based Empathetic Conversational Model (2021)

Abstract: Artificial intelligence(AI) has come a long way in aiding the user requirements in many fields and domains. However, the current AI systems do not generate human- like response for user query. Research in these areas have started gaining traction recently with explorations on persona or empathy based response selection. But the combination of both the parameters in an open domain haven’t been explored in detail by the research community. The current work highlights the effect of persona on empathetic response. This research paper concentrates on improving the response selection model for PEC dataset, containing both persona information and empathetic response. This is achieved using an enhanced multi relationship extractor and phrase based information for the selection of response.

## introduction empathetic response generation refers to the ability of a system to understand the people mentality or the feelings of a user and provide an appropriate response. in the area of nlp, empathetic conversational models have shown a positive impact on the user compared to normal responses @xcite ; @xcite ; @xcite ; @xcite ].empathetic response creates a personal connect with the user and achieves significantly higher engagement by providing seamless conversational experience as shown by this example 1. you can see how empathetic response aides in different kind of engagement with the user and also provide suggestions. recent years have seen an increase in exploration on empathetic response generations using neural conversational models. @xcite have developed caire to generate empathetic response using user emotions for better connections with the * samsung r&d institute india -bangalore figure 1 : normal chatbot vs empathetic chatbot user. @xcite have used causal emotional information to understand a particular user emotional state and correspondingly learn response for each emotion class. @xcite have developed an ai model named xiaoice for better human understanding and communication. @xcite has created custom datasets for the said problems named empatheticdialogues (ed). these dataset has proven that pre-trained retrieval models like bert and its variants are able to reply with more empathy when trained with such dataset. the above explorations doesn't take into account the persona of the user.as persona aides in better conversational response, this area started gaining traction by the research community. @xcite developed crisisbot where it uses the persona of the user in the conversation to provide complex response to train hotline counselors for suicide prevention task. @xcite explored the way to generate sustainable and coherent response, using persona in a conversation, where each request will have many possible responses. @xcite developed an emotion-affective open domain chatbot where they use knowledge graph to extract personal information and incorporate into the system for consistent personality. the presence of persona have shown impact in the conversational response. hence, exploration on the impact of persona on empathetic response started gaining traction. persona have shown to impact empathetic natural language generation capability. we have noticed that empathetic response of system is different from two different users for the same input user utterance or query. @xcite incorporated persona with empathy on response generation for several dataset but the impact of persona on empathy is not explored. @xcite has presented a novel large-scale multi-turn persona-based empathetic conversation (pec) using two contrasting sentimental domains from social media reddit. they have proposed novel cobert architecture (combination of bert with basic co-attention mechanism) to select the appropriate response for the post and penalise opposite response pairs. this is the first approach where persona information is used to influence the empathetic response selection. the current architecture takes the full user context, personas and response for gathering bi-attention and selecting appropriate response. co-attention won't be able to capture phrase level impact on the response selection. moreover the influence of the different phrases from different positions might not be captured well. to address this issue, we are proposing multi relationship extraction using bert for influencing the selection of appropriate response and penalising the un-important ones. in summary, the contributions of the papers is summarized as below • we propose phrase importance planner to extract n-gram impact of the phrase in unigram. ## pec dataset this section explains the high level information on pec dataset gathered by @xcite . for full detailed analysis, please refer to the above paper. pec dataset is available in huggingface as 1 . ## experiments this section explains the baseline models, experimentation and model comparisons. ## conclusion we are able to observe that additional phrase level information flow, both for user context as well as persona, aided in better relationship building between response and context as well response and persona which in turn aided in better response selection. in addition the multi head attention aided in multi phrase positional information capture which resulted in better learning representation.