# Affective Decoding for Empathetic Response Generation

arXiv: https://arxiv.org/abs/2108.08102 · HTML: https://mlsanity.com/paper/2108.08102/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [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)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [A Knowledge-Grounded Neural Conversation Model](https://mlsanity.com/paper/1702.01932/) (2017)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis](https://mlsanity.com/paper/1901.10623/) (2019)
- [RankME: Reliable Human Ratings for Natural Language Generation](https://mlsanity.com/paper/1803.05928/) (2018)
- [Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1804.06512/) (2018)
- [Affect-LM: A Neural Language Model for Customizable Affective Text Generation](https://mlsanity.com/paper/1704.06851/) (2017)
- [Towards Best Experiment Design for Evaluating Dialogue System Output](https://mlsanity.com/paper/1909.10122/) (2019)
- [Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition](https://mlsanity.com/paper/2004.10663/) (2020)
- [Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation](https://mlsanity.com/paper/2011.01136/) (2020)
- [A Stable Variational Autoencoder for Text Modelling](https://mlsanity.com/paper/1911.05343/) (2019)

## Built on by (most cited first, 4 of 4)

- [Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics](https://mlsanity.com/paper/2210.10618/) (2022)
- [Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy](https://mlsanity.com/paper/2205.07233/) (2022)
- [A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing](https://mlsanity.com/paper/2210.16604/) (2022)
- [Modeling Empathetic Alignment in Conversation](https://mlsanity.com/paper/2405.00948/) (2024)

## Abstract (arXiv)

Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding.