Abstract
Large language models underestimate the impact of negations on how much they change the meaning of a sentence. Therefore, learned evaluation metrics based on these models are insensitive to negations. In this paper, we propose NegBLEURT, a negation-aware version of the BLEURT evaluation metric. For that, we designed a rule-based sentence negation tool and used it to create the CANNOT negation evaluation dataset. Based on this dataset, we fine-tuned a sentence transformer and an evaluation metric to improve their negation sensitivity. Evaluating these models on existing benchmarks shows that our fine-tuned models outperform existing metrics on the negated sentences by far while preserving their base models’ performances on other perturbations.- Anthology ID:
- 2023.inlg-main.12
- Volume:
- Proceedings of the 16th International Natural Language Generation Conference
- Month:
- September
- Year:
- 2023
- Address:
- Prague, Czechia
- Editors:
- C. Maria Keet, Hung-Yi Lee, Sina Zarrieß
- Venues:
- INLG | SIGDIAL
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 163–175
- Language:
- URL:
- https://aclanthology.org/2023.inlg-main.12
- DOI:
- 10.18653/v1/2023.inlg-main.12
- Cite (ACL):
- Miriam Anschütz, Diego Miguel Lozano, and Georg Groh. 2023. This is not correct! Negation-aware Evaluation of Language Generation Systems. In Proceedings of the 16th International Natural Language Generation Conference, pages 163–175, Prague, Czechia. Association for Computational Linguistics.
- Cite (Informal):
- This is not correct! Negation-aware Evaluation of Language Generation Systems (Anschütz et al., INLG-SIGDIAL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.inlg-main.12.pdf