# On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems

arXiv: https://arxiv.org/abs/2104.06335 · HTML: https://mlsanity.com/paper/2104.06335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [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)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses](https://mlsanity.com/paper/1708.07149/) (2017)
- [Re-evaluating ADEM: A Deeper Look at Scoring Dialogue Responses](https://mlsanity.com/paper/1902.08832/) (2019)
- [deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets](https://mlsanity.com/paper/1506.06863/) (2015)
- [There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction](https://mlsanity.com/paper/1610.02124/) (2016)
- [Adversarial Evaluation of Dialogue Models](https://mlsanity.com/paper/1701.08198/) (2017)
- [On the Evaluation of Dialogue Systems with Next Utterance Classification](https://mlsanity.com/paper/1605.05414/) (2016)
- [Towards a Metric for Automated Conversational Dialogue System Evaluation and Improvement](https://mlsanity.com/paper/1909.12066/) (2019)

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

- [Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple Metric](https://mlsanity.com/paper/2206.01823/) (2022)

## Abstract (arXiv)

Automatically evaluating text-based, non-task-oriented dialogue systems (i.e., `chatbots') remains an open problem. Previous approaches have suffered challenges ranging from poor correlation with human judgment to poor generalization and have often required a gold standard reference for comparison or human-annotated data. Extending existing evaluation methods, we propose that a metric based on linguistic features may be able to maintain good correlation with human judgment and be interpretable, without requiring a gold-standard reference or human-annotated data. To support this proposition, we measure and analyze various linguistic features on dialogues produced by multiple dialogue models. We find that the features' behaviour is consistent with the known properties of the models tested, and is similar across domains. We also demonstrate that this approach exhibits promising properties such as zero-shot generalization to new domains on the related task of evaluating response relevance.