The ExBAN dataset: a corpus of NL explanations generated by crowd-sourced participants presented with the task of explaining simple Bayesian Network (BN) graphical representations. These explanations, in a separate collection effort, are rated for clarity and informativeness.
If you use this dataset in your work, please cite the following paper:
Clinciu, Miruna-Adriana, Arash Eshghi, and Helen Hastie. "A Study of Automatic Metrics for the Evaluation of Natural Language Explanations." Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Association for Computational Linguistics, April 2021, Online, pp. 2376-2387. Available at: https://www.aclweb.org/anthology/2021.eacl-main.202.
Abstract: As transparency becomes key for robotics and AI, it will be necessary to evaluate the methods through which transparency is provided, including automatically generated natural language (NL) explanations. Here, we explore parallels between the generation of such explanations and the much-studied field of evaluation of Natural Language Generation (NLG). Specifically, we investigate which of the NLG evaluation measures map well to explanations. We present the ExBAN corpus: a crowd-sourced corpus of NL explanations for Bayesian Networks. We run correlations comparing human subjective ratings with NLG automatic measures. We find that embedding-based automatic NLG evaluation methods, such as BERTScore and BLEURT, have a higher correlation with human ratings, compared to word-overlap metrics, such as BLEU and ROUGE. This work has implications for Explainable AI and transparent robotic and autonomous systems.
@inproceedings{clinciu-etal-2021-study,
title = "A Study of Automatic Metrics for the Evaluation of Natural Language Explanations",
author = "Clinciu, Miruna-Adriana and
Eshghi, Arash and
Hastie, Helen",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.eacl-main.202",
pages = "2376--2387",
abstract = "As transparency becomes key for robotics and AI, it will be necessary to evaluate the methods through which transparency is provided, including automatically generated natural language (NL) explanations. Here, we explore parallels between the generation of such explanations and the much-studied field of evaluation of Natural Language Generation (NLG). Specifically, we investigate which of the NLG evaluation measures map well to explanations. We present the ExBAN corpus: a crowd-sourced corpus of NL explanations for Bayesian Networks. We run correlations comparing human subjective ratings with NLG automatic measures. We find that embedding-based automatic NLG evaluation methods, such as BERTScore and BLEURT, have a higher correlation with human ratings, compared to word-overlap metrics, such as BLEU and ROUGE. This work has implications for Explainable AI and transparent robotic and autonomous systems.",
}