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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics |
| Subtitle of host publication | Main Volume |
| Publisher | Association for Computational Linguistics |
| Pages | 2376-2387 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781954085022 |
| Publication status | Published - Apr 2021 |
| Event | 16th Conference of the European Chapter of the Associationfor Computational Linguistics 2021 - Virtual, Online Duration: 19 Apr 2021 → 23 Apr 2021 |
Conference
| Conference | 16th Conference of the European Chapter of the Associationfor Computational Linguistics 2021 |
|---|---|
| Abbreviated title | EACL 2021 |
| City | Virtual, Online |
| Period | 19/04/21 → 23/04/21 |
ASJC Scopus subject areas
- Software
- Computational Theory and Mathematics
- Linguistics and Language