# A Comprehensive Assessment of Dialog Evaluation Metrics

arXiv: https://arxiv.org/abs/2106.03706 · HTML: https://mlsanity.com/paper/2106.03706/

## Key facts

- Cited by 90 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 68 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 30 of 46)

- [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)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [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)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (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)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [QuestEval: Summarization Asks for Fact-based Evaluation](https://mlsanity.com/paper/2103.12693/) (2021)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation](https://mlsanity.com/paper/2005.00456/) (2020)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable](https://mlsanity.com/paper/1910.07931/) (2019)
- [What makes a good conversation? How controllable attributes affect human judgments](https://mlsanity.com/paper/1902.08654/) (2019)
- [Unsupervised Evaluation of Interactive Dialog with DialoGPT](https://mlsanity.com/paper/2006.12719/) (2020)
- [Survey on Evaluation Methods for Dialogue Systems](https://mlsanity.com/paper/1905.04071/) (2019)
- [Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses](https://mlsanity.com/paper/1708.07149/) (2017)
- [RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems](https://mlsanity.com/paper/1701.03079/) (2017)
- [BLEU is Not Suitable for the Evaluation of Text Simplification](https://mlsanity.com/paper/1810.05995/) (2018)
- [GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems](https://mlsanity.com/paper/2010.03994/) (2020)
- [Dialogue Response Ranking Training with Large-Scale Human Feedback Data](https://mlsanity.com/paper/2009.06978/) (2020)

## Built on by (most cited first, 30 of 90)

- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models](https://mlsanity.com/paper/2305.13711/) (2023)
- [Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents](https://mlsanity.com/paper/2201.04723/) (2022)
- [A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators](https://mlsanity.com/paper/2312.15407/) (2023)
- [InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning](https://mlsanity.com/paper/2205.12673/) (2022)
- [Controllable Mixed-Initiative Dialogue Generation through Prompting](https://mlsanity.com/paper/2305.04147/) (2023)
- [Automatic Evaluation and Moderation of Open-domain Dialogue Systems](https://mlsanity.com/paper/2111.02110/) (2021)
- [Proactive Assistant Dialogue Generation from Streaming Egocentric Videos](https://mlsanity.com/paper/2506.05904/) (2025)
- [The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues](https://mlsanity.com/paper/2306.06941/) (2023)
- [Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation](https://mlsanity.com/paper/2110.08501/) (2021)
- [Spurious Correlations in Reference-Free Evaluation of Text Generation](https://mlsanity.com/paper/2204.09890/) (2022)
- [DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations](https://mlsanity.com/paper/2203.09711/) (2022)
- [Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations](https://mlsanity.com/paper/2405.17974/) (2024)
- [What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation](https://mlsanity.com/paper/2203.13927/) (2022)
- [NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist](https://mlsanity.com/paper/2305.08566/) (2023)
- [Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities](https://mlsanity.com/paper/2206.05017/) (2022)
- [MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation](https://mlsanity.com/paper/2112.07194/) (2021)
- [Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges](https://mlsanity.com/paper/2203.10012/) (2022)
- [What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys](https://mlsanity.com/paper/2205.10977/) (2022)
- [FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation](https://mlsanity.com/paper/2210.13832/) (2022)
- [xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark](https://mlsanity.com/paper/2310.08958/) (2023)
- [Exploring Dense Retrieval for Dialogue Response Selection](https://mlsanity.com/paper/2110.06612/) (2021)
- [Approximating Online Human Evaluation of Social Chatbots with Prompting](https://mlsanity.com/paper/2304.05253/) (2023)
- [PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison](https://mlsanity.com/paper/2404.01015/) (2024)
- [Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4](https://mlsanity.com/paper/2306.12794/) (2023)
- [On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation](https://mlsanity.com/paper/2407.03841/) (2024)
- [Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs](https://mlsanity.com/paper/2408.10902/) (2024)
- [Towards Understanding the Robustness of LLM-based Evaluations under Perturbations](https://mlsanity.com/paper/2412.09269/) (2024)
- [Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations](https://mlsanity.com/paper/2312.13871/) (2023)

## Abstract (arXiv)

Automatic evaluation metrics are a crucial component of dialog systems research. Standard language evaluation metrics are known to be ineffective for evaluating dialog. As such, recent research has proposed a number of novel, dialog-specific metrics that correlate better with human judgements. Due to the fast pace of research, many of these metrics have been assessed on different datasets and there has as yet been no time for a systematic comparison between them. To this end, this paper provides a comprehensive assessment of recently proposed dialog evaluation metrics on a number of datasets. In this paper, 23 different automatic evaluation metrics are evaluated on 10 different datasets. Furthermore, the metrics are assessed in different settings, to better qualify their respective strengths and weaknesses. Metrics are assessed (1) on both the turn level and the dialog level, (2) for different dialog lengths, (3) for different dialog qualities (e.g., coherence, engaging), (4) for different types of response generation models (i.e., generative, retrieval, simple models and state-of-the-art models), (5) taking into account the similarity of different metrics and (6) exploring combinations of different metrics. This comprehensive assessment offers several takeaways pertaining to dialog evaluation metrics in general. It also suggests how to best assess evaluation metrics and indicates promising directions for future work.