# Generating Multiple Diverse Responses for Short-Text Conversation

arXiv: https://arxiv.org/abs/1811.05696 · HTML: https://mlsanity.com/paper/1811.05696/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 13 of 13)

- [DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation](https://mlsanity.com/paper/2204.13031/) (2022)
- [FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation](https://mlsanity.com/paper/2211.00250/) (2022)
- [PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation](https://mlsanity.com/paper/2208.08110/) (2022)
- [An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation](https://mlsanity.com/paper/2209.14627/) (2022)
- [DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization](https://mlsanity.com/paper/2207.05631/) (2022)
- [Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation](https://mlsanity.com/paper/2109.01484/) (2021)
- [Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward](https://mlsanity.com/paper/2109.06717/) (2021)
- [REAMsharp: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation](https://mlsanity.com/paper/2105.14488/) (2021)
- [Incorporating Causal Analysis into Diversified and Logical Response Generation](https://mlsanity.com/paper/2209.09482/) (2022)
- [An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters](https://mlsanity.com/paper/2209.00200/) (2022)
- [Partially Randomizing Transformer Weights for Dialogue Response Diversity](https://mlsanity.com/paper/2311.10943/) (2023)
- [Toxic Subword Pruning for Dialogue Response Generation on Large Language Models](https://mlsanity.com/paper/2410.04155/) (2024)
- [Empathetic Response Generation with State Management](https://mlsanity.com/paper/2205.03676/) (2022)

## Abstract (arXiv)

Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the input post to its output response. However, a given post is often associated with multiple replies simultaneously in real applications. Previous research on this task mainly focuses on improving the relevance and informativeness of the top one generated response for each post. Very few works study generating multiple accurate and diverse responses for the same post. In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously. A reinforcement learning algorithm is designed to solve our model. Experiments on two short-text conversation tasks validate that the multiple responses generated by our model obtain higher quality and larger diversity compared with various state-of-the-art generative models.