# Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation

arXiv: https://arxiv.org/abs/2206.04937 · HTML: https://mlsanity.com/paper/2206.04937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [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)
- [Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings](https://mlsanity.com/paper/1904.10635/) (2019)
- [Designing Precise and Robust Dialogue Response Evaluators](https://mlsanity.com/paper/2004.04908/) (2020)
- [Dialogue Act Classification with Context-Aware Self-Attention](https://mlsanity.com/paper/1904.02594/) (2019)
- [Why are Sequence-to-Sequence Models So Dull? Understanding the Low-Diversity Problem of Chatbots](https://mlsanity.com/paper/1809.01941/) (2018)
- [Contextual Dialogue Act Classification for Open-Domain Conversational Agents](https://mlsanity.com/paper/2005.13804/) (2020)

## Built on by (most cited first, 1 of 1)

- [Modeling the One-to-Many Property in Open-Domain Dialogue with LLMs](https://mlsanity.com/paper/2506.15131/) (2025)

## Abstract (arXiv)

We aim to overcome the lack of diversity in responses of current dialogue systems and to develop a dialogue system that is engaging as a conversational partner. We propose a generator-evaluator model that evaluates multiple responses generated by a response generator and selects the best response by an evaluator. By generating multiple responses, we obtain diverse responses. We conduct human evaluations to compare the output of the proposed system with that of a baseline system. The results of the human evaluations showed that the proposed system's responses were often judged to be better than the baseline system's, and indicated the effectiveness of the proposed method.