ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and
  Emotion Modeling
Omama Hamad Ali Hamdi Khaled Shaban
Effective feature representations play a critical role in enhancing the
performance of text generation models that rely on deep neural networks.
However, current approaches suffer from several drawbacks, such as the
inability to capture the deep semantics of language and sensitivity to minor
input variations, resulting in significant changes in the generated text. In
this paper, we present a novel solution to these challenges by employing a
mixture of experts, multiple encoders, to offer distinct perspectives on the
emotional state of the user's utterance while simultaneously enhancing
performance. We propose an end-to-end model architecture called ASEM that
performs emotion analysis on top of sentiment analysis for open-domain
chatbots, enabling the generation of empathetic responses that are fluent and
relevant. In contrast to traditional attention mechanisms, the proposed model
employs a specialized attention strategy that uniquely zeroes in on sentiment
and emotion nuances within the user's utterance. This ensures the generation of
context-rich representations tailored to the underlying emotional tone and
sentiment intricacies of the text. Our approach outperforms existing methods
for generating empathetic embeddings, providing empathetic and diverse
responses. The performance of our proposed model significantly exceeds that of
existing models, enhancing emotion detection accuracy by 6.2% and lexical
diversity by 1.4%.