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, pages 2376–2387 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 2376 A Study of Automatic Metrics for the Evaluation of Natural Language Explanations Miruna-Adriana Clinciu Edinburgh Centre for Robotics Heriot-Watt University University of Edinburgh mc191@hw.ac.uk Arash Eshghi Heriot-Watt University Edinburgh, United Kingdom a.eshghi@hw.ac.uk Helen Hastie Heriot-Watt University Edinburgh, United Kingdom h.hastie@hw.ac.uk 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. 1 Introduction The machine learning models and algorithms underlying today’s AI and robotic systems are increasingly complex with their internal operations and decision-making processes ever more opaque. This opacity is not just an issue for the end-user, but also the creators and analysts of these systems. As we move towards building safer and more ethical systems, this lack of transparency needs to be addressed. One key trait of a transparent system is its ability to be able to explain its deductions and articulate the reasons for its actions in Natural Language (NL). As the area of Explainable AI (XAI) grows and is mandated (cf. the EU General Data Protection Regulation's “right to explanation” (Commission, 2018) and standardisation (cf. IEEE forthcoming standard on Transparency (P7001)), it has become ever more important to be able to evaluate the quality of the NL explanations themselves, as well as the AI algorithms they explain. Furthermore, the importance of evaluating explanations has been emphasised by researchers within the social cognitive sciences (Leake, 2014; Zemla et al., 2017; Doshi-Velez and Kim, 2017). To date, explanations have mostly been evaluated by collecting human judgements, which is both time-consuming and costly. Here, we view generating explanations as a special case of Natural Language Generation (NLG), and so we explore mapping existing automatic evaluation methods for NLG onto explanations. We study whether general, domain-independent evaluation metrics within NLG are sensitive enough to capture the peculiarities inherent in NL explanations (Kumar and Talukdar, 2020), such as causality; or whether NL explanations constitute a sui-generis category, thus requiring their own automatic evaluation methods and criteria. In this paper, we firstly present 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 were subsequently rated for Clarity and …

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