IAE mp: Intent-aware Empathetic Response Generation (2022)

Abstract: In the domain of virtual assistants or conversational systems, it is important to empathise with the user. Being empathetic involves understanding the emotion of the ongoing dialogue and responding to the situation with empathy. We propose a novel approach for empathetic response generation, which leverages predicted intents for future response and prompts the encoder-decoder model to improve empathy in generated responses. Our model exploits the combination of dialogues and their respective emotions to generate empathetic response. As responding intent plays an important part in our generation, we also employ one or more intents to generate responses with relevant empathy. We achieve improved human and automated metrics, compared to the baselines.

## introduction empathy is the ability to understand, feel and respond to the feelings of another with empathy. it is a fundamental human trait for smooth social interactions and is paramount to designing conversational systems (especially ones that target much more than task-oriented). the complexity of empathetic behaviour makes it challenging to design an empathetic system with computational paradigms. over the last few years, with the development of auto-regressive language models to generate texts, most of the existing neural conversational systems can generate syntactically and contextually well-formed responses. yet, fine-grained control in terms of empathy gets less attention than the semantics of generations. in previous studies, encoder-decoder transformer architectures have been used @xcite along with emotion understanding to generate empathetic dialogues so that * equal contribution speaker: christmas was the best time of year back in the day! generate listener response with intents: acknowledging, nostalgic empathetic response: that's so true! i used to love it when i was a kid. speaker: i recently spoke with my exgirlfriend on the phone. the conversation went pretty well, and it reminded me of my past experiences with her. generate listener response with intents: encouraging, consoling empathetic response: that's good to hear. i hope things work out for you. the model can be more perceptive towards the emotion of the speaker. @xcite employs a semantic discriminator and an emotion discriminator to interact with the user feedback. @xcite uses an emotional context graph, an emotional context encoder and an emotion-dependency decoder. the context graph is constructed by interaction of dialogue data with external knowledge, and the context encoder employs a graph-aware transformer. a limited number of works have employed intents conditioned on emotions of the previous utterances for a guided empathetic generation. intents are fundamentally different from emotions, wherein emotions are the feelings of the speaker of the utterance, and the intent is a response strategy. for example, consoling and enquiring can be responding intents in case of a frustrating situation in the speaker's life. we propose a novel approach to leverage these intents of responding in a conversation, which are predicted from a classifier -built on dialogue history and respective emotion labels. using the predicted intents, we structure the input that can guide a pre-trained encoder-decoder language model. additionally, we do ablation studies on the benefits of using one or more intents and observe a sweet spot of 2 intents giving the best generations with empathy; for the brevity of paper length, we will include results only from 1 and 2 intents. from what we observe, most existing works require custom transformer models to be trained from scratch or employ a strategy of using external knowledge sources to provide emotional grounding for generations. our approach is easily adaptable to new domains as it tries to probe the pre-trained models and only needs fine-tuning that does not take long. we show with automatic and human evaluations that our models achieve significant improvements over the baselines discussed in further sections, which along with the adaptability of this approach, highlights the inherent potential. ## conclusion we propose an easily adaptable approach to generating empathetic responses in a conversational setting, where we leverage emotions of dialogue history and intents to generate responses. we show empirically that responses generated with a mixture of emotions tend to be better in our experiments. our automatic and human evaluations show that our models with single intent and models with a mixture of intents perform significantly better compared to existing works.