# Affective Neural Response Generation

arXiv: https://arxiv.org/abs/1709.03968 · HTML: https://mlsanity.com/paper/1709.03968/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2017.

## Built on by (most cited first, 17 of 17)

- [OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts](https://mlsanity.com/paper/2109.12761/) (2021)
- [Automatically Select Emotion for Response via Personality-affected Emotion Transition](https://mlsanity.com/paper/2106.15846/) (2021)
- [Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries](https://mlsanity.com/paper/2109.09195/) (2021)
- [Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation](https://mlsanity.com/paper/2301.04907/) (2023)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [Simulated Annealing for Emotional Dialogue Systems](https://mlsanity.com/paper/2109.10715/) (2021)
- [Task-oriented Dialogue Systems: performance vs. quality-optima, a review](https://mlsanity.com/paper/2112.11176/) (2021)
- [A Review of Affective Generation Models](https://mlsanity.com/paper/2202.10763/) (2022)
- [Personality-affected Emotion Generation in Dialog Systems](https://mlsanity.com/paper/2404.07229/) (2024)
- [Expressivity and Speech Synthesis](https://mlsanity.com/paper/2404.19363/) (2024)
- [CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective](https://mlsanity.com/paper/2205.03540/) (2022)
- [SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization](https://mlsanity.com/paper/2503.23303/) (2025)
- [A Unified Framework for Emotion Identification and Generation in Dialogues](https://mlsanity.com/paper/2205.15513/) (2022)
- [Towards a Sentiment-Aware Conversational Agent](https://mlsanity.com/paper/2207.11774/) (2022)
- [Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems](https://mlsanity.com/paper/2208.08017/) (2022)
- [Recent Progress in Conversational AI](https://mlsanity.com/paper/2204.09719/) (2022)
- [Predicting Evoked Emotions in Conversations](https://mlsanity.com/paper/2401.00383/) (2023)

## Abstract (arXiv)

Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by proposing three novel ways to incorporate affective/emotional aspects into long short term memory (LSTM) encoder-decoder neural conversation models: (1) affective word embeddings, which are cognitively engineered, (2) affect-based objective functions that augment the standard cross-entropy loss, and (3) affectively diverse beam search for decoding. Experiments show that these techniques improve the open-domain conversational prowess of encoder-decoder networks by enabling them to produce emotionally rich responses that are more interesting and natural.