On the interaction of automatic evaluation and task framing in headline style transfer
Lorenzo De Mattei, Michele Cafagna, Huiyuan Lai, Felice Dell’Orletta, Malvina Nissim, Albert Gatt
Abstract
An ongoing debate in the NLG community concerns the best way to evaluate systems, with human evaluation often being considered the most reliable method, compared to corpus-based metrics. However, tasks involving subtle textual differences, such as style transfer, tend to be hard for humans to perform. In this paper, we propose an evaluation method for this task based on purposely-trained classifiers, showing that it better reflects system differences than traditional metrics such as BLEU.- Anthology ID:
- 2020.evalnlgeval-1.5
- Volume:
- Proceedings of the 1st Workshop on Evaluating NLG Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Online (Dublin, Ireland)
- Editors:
- Shubham Agarwal, Ondřej Dušek, Sebastian Gehrmann, Dimitra Gkatzia, Ioannis Konstas, Emiel Van Miltenburg, Sashank Santhanam
- Venue:
- EvalNLGEval
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 38–43
- Language:
- URL:
- https://aclanthology.org/2020.evalnlgeval-1.5
- DOI:
- Cite (ACL):
- Lorenzo De Mattei, Michele Cafagna, Huiyuan Lai, Felice Dell’Orletta, Malvina Nissim, and Albert Gatt. 2020. On the interaction of automatic evaluation and task framing in headline style transfer. In Proceedings of the 1st Workshop on Evaluating NLG Evaluation, pages 38–43, Online (Dublin, Ireland). Association for Computational Linguistics.
- Cite (Informal):
- On the interaction of automatic evaluation and task framing in headline style transfer (Mattei et al., EvalNLGEval 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.evalnlgeval-1.5.pdf
- Code
- michelecafagna26/CHANGE-IT