# ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling

arXiv: https://arxiv.org/abs/2402.16194 · HTML: https://mlsanity.com/paper/2402.16194/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [CEM: Commonsense-aware Empathetic Response Generation](https://mlsanity.com/paper/2109.05739/) (2021)
- [Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/1905.05812/) (2019)
- [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://mlsanity.com/paper/1811.00207/) (2018)
- [EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments](https://mlsanity.com/paper/2111.00310/) (2021)
- [Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis](https://mlsanity.com/paper/1911.12569/) (2019)

## Built on by (most cited first, 7 of 7)

- [Kardia-R1: Unleashing LLMs to Reason toward Understanding and Empathy for Emotional Support via Rubric-as-Judge Reinforcement Learning](https://mlsanity.com/paper/2512.01282/) (2025)
- [ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework](https://mlsanity.com/paper/2409.10289/) (2024)
- [LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms](https://mlsanity.com/paper/2412.13765/) (2024)
- [ERPA: Efficient RPA Model Integrating OCR and LLMs for Intelligent Document Processing](https://mlsanity.com/paper/2412.19840/) (2024)
- [An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction](https://mlsanity.com/paper/2509.02446/) (2025)
- [Arabic Large Language Models for Medical Text Generation](https://mlsanity.com/paper/2509.10095/) (2025)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)

## Abstract (arXiv)

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%.