RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question

Alireza Mohammadshahi, Thomas Scialom, Majid Yazdani, Pouya Yanki, Angela Fan, James Henderson, Marzieh Saeidi


Abstract
Existing metrics for evaluating the quality of automatically generated questions such as BLEU, ROUGE, BERTScore, and BLEURT compare the reference and predicted questions, providing a high score when there is a considerable lexical overlap or semantic similarity between the candidate and the reference questions. This approach has two major shortcomings. First, we need expensive human-provided reference questions. Second, it penalises valid questions that may not have high lexical or semantic similarity to the reference questions. In this paper, we propose a new metric, RQUGE, based on the answerability of the candidate question given the context. The metric consists of a question-answering and a span scorer modules, using pre-trained models from existing literature, thus it can be used without any further training. We demonstrate that RQUGE has a higher correlation with human judgment without relying on the reference question. Additionally, RQUGE is shown to be more robust to several adversarial corruptions. Furthermore, we illustrate that we can significantly improve the performance of QA models on out-of-domain datasets by fine-tuning on synthetic data generated by a question generation model and reranked by RQUGE.
Anthology ID:
2023.findings-acl.428
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6845–6867
Language:
URL:
https://aclanthology.org/2023.findings-acl.428
DOI:
10.18653/v1/2023.findings-acl.428
Bibkey:
Cite (ACL):
Alireza Mohammadshahi, Thomas Scialom, Majid Yazdani, Pouya Yanki, Angela Fan, James Henderson, and Marzieh Saeidi. 2023. RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6845–6867, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question (Mohammadshahi et al., Findings 2023)
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PDF:
https://aclanthology.org/2023.findings-acl.428.pdf
Video:
 https://aclanthology.org/2023.findings-acl.428.mp4