# Affect-Driven Dialog Generation

arXiv: https://arxiv.org/abs/1904.02793 · HTML: https://mlsanity.com/paper/1904.02793/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 30 of 50)

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- [InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation](https://mlsanity.com/paper/2112.01589/) (2021)
- [COGMEN: COntextualized GNN based Multimodal Emotion recognitioN](https://mlsanity.com/paper/2205.02455/) (2022)
- [Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data](https://mlsanity.com/paper/2212.09171/) (2022)
- [A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions](https://mlsanity.com/paper/2103.12711/) (2021)
- [An End-to-End Network for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2103.01544/) (2021)
- [Automatically Select Emotion for Response via Personality-affected Emotion Transition](https://mlsanity.com/paper/2106.15846/) (2021)
- [Improving Multimodal fusion via Mutual Dependency Maximisation](https://mlsanity.com/paper/2109.00922/) (2021)
- [Unsupervised Layer-wise Score Aggregation for Textual OOD Detection](https://mlsanity.com/paper/2302.09852/) (2023)
- [Learning Disentangled Textual Representations via Statistical Measures of Similarity](https://mlsanity.com/paper/2205.03589/) (2022)
- [Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks](https://mlsanity.com/paper/2305.10284/) (2023)
- [Beam Search with Bidirectional Strategies for Neural Response Generation](https://mlsanity.com/paper/2110.03389/) (2021)
- [Code-switched inspired losses for generic spoken dialog representations](https://mlsanity.com/paper/2108.12465/) (2021)
- [The Glass Ceiling of Automatic Evaluation in Natural Language Generation](https://mlsanity.com/paper/2208.14585/) (2022)
- [Fine-Grained Emotion Prediction by Modeling Emotion Definitions](https://mlsanity.com/paper/2107.12135/) (2021)
- [Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts](https://mlsanity.com/paper/2112.01938/) (2021)
- [EmoUS: Simulating User Emotions in Task-Oriented Dialogues](https://mlsanity.com/paper/2306.01579/) (2023)
- [MOPO: Multi-Objective Prompt Optimization for Affective Text Generation](https://mlsanity.com/paper/2412.12948/) (2024)
- [Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge](https://mlsanity.com/paper/2109.03004/) (2021)
- [Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation](https://mlsanity.com/paper/2408.02976/) (2024)
- [Empathetic Dialog Generation with Fine-Grained Intents](https://mlsanity.com/paper/2105.06829/) (2021)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditions](https://mlsanity.com/paper/2307.14004/) (2023)
- [The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents](https://mlsanity.com/paper/2309.15311/) (2023)
- [Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models](https://mlsanity.com/paper/2310.13998/) (2023)
- [Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation](https://mlsanity.com/paper/2109.07779/) (2021)
- [Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations](https://mlsanity.com/paper/2211.03742/) (2022)

## Abstract (arXiv)

The majority of current systems for end-to-end dialog generation focus on response quality without an explicit control over the affective content of the responses. In this paper, we present an affect-driven dialog system, which generates emotional responses in a controlled manner using a continuous representation of emotions. The system achieves this by modeling emotions at a word and sequence level using: (1) a vector representation of the desired emotion, (2) an affect regularizer, which penalizes neutral words, and (3) an affect sampling method, which forces the neural network to generate diverse words that are emotionally relevant. During inference, we use a reranking procedure that aims to extract the most emotionally relevant responses using a human-in-the-loop optimization process. We study the performance of our system in terms of both quantitative (BLEU score and response diversity), and qualitative (emotional appropriateness) measures.