SEEC and CHASE: An emotion-cause pair-oriented approach and conversational dataset with heterogeneous emotions for empathetic response generation

Abstract

Empathetic Response Generation (ERG) in dialog agents has gained tremendous attention in the recent past. Among various methods used to impart empathy in the generated response, the contemporary one is using the emotion cause. Previous works of ERG have 2 major flaws: (1) usage of the emotion detected using the entire conversation to generate the empathetic response is heavily misleading as the speaker might be going through a completely different emotion in his last utterance. This erroneous emotion detection, in turn, leads to incorrect detection of the cause of that emotion. (2) Usage of entire utterance has proven to be inefficient in the cause extraction task. Consequently, existing works fail to capture multiple emotion clauses and their corresponding emotion causes for efficient ERG. In our work, we introduce a new dataset,11The dataset will be made available at https://drive.google.com/drive/folders/1ozYeuACo_He75w0XoNWZntMZ3FkiTHPB?usp=sharing.CHASE, which is a compilation of conversations extracted from various plays to highlight the above-mentioned change in emotion and how one can show empathy in such a case. In this dataset, we use Dr. Brene Brown’s 22https://brenebrown.com/. notion of empathy on how to administer this change in emotion in the response and still sound empathetic by generating golden responses for each conversation. To address the aforementioned flaws, we also propose a model, SEEC, that utilizes the Emotion-Cause Pair Extraction task on the conversation to find various {emotion clause, cause clause} pairs and use these to impart empathy appropriately to the responses. Our qualitative and quantitative results prove the efficiency in generating enhanced empathetic responses of both SEEC and CHASE.