Abstract
We present new state-of-the-art benchmarks for paraphrase detection on all six languages in the Opusparcus sentential paraphrase corpus: English, Finnish, French, German, Russian, and Swedish. We reach these baselines by fine-tuning BERT. The best results are achieved on smaller and cleaner subsets of the training sets than was observed in previous research. Additionally, we study a translation-based approach that is competitive for the languages with more limited and noisier training data.- Anthology ID:
- 2021.wnut-1.32
- Volume:
- Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 291–296
- Language:
- URL:
- https://aclanthology.org/2021.wnut-1.32
- DOI:
- 10.18653/v1/2021.wnut-1.32
- Cite (ACL):
- Teemu Vahtola, Mathias Creutz, Eetu Sjöblom, and Sami Itkonen. 2021. Coping with Noisy Training Data Labels in Paraphrase Detection. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 291–296, Online. Association for Computational Linguistics.
- Cite (Informal):
- Coping with Noisy Training Data Labels in Paraphrase Detection (Vahtola et al., WNUT 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2021.wnut-1.32.pdf
- Data
- OpenSubtitles, Opusparcus