Abstract
Splitting and rephrasing a complex sentence into several shorter sentences that convey the same meaning is a challenging problem in NLP. We show that while vanilla seq2seq models can reach high scores on the proposed benchmark (Narayan et al., 2017), they suffer from memorization of the training set which contains more than 89% of the unique simple sentences from the validation and test sets. To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8.68 BLEU and fostering further progress on the task.- Anthology ID:
- P18-2114
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 719–724
- Language:
- URL:
- https://aclanthology.org/P18-2114
- DOI:
- 10.18653/v1/P18-2114
- Cite (ACL):
- Roee Aharoni and Yoav Goldberg. 2018. Split and Rephrase: Better Evaluation and Stronger Baselines. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 719–724, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Split and Rephrase: Better Evaluation and Stronger Baselines (Aharoni & Goldberg, ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P18-2114.pdf
- Code
- biu-nlp/sprp-acl2018