Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models
Satoru Katsumata, Yukio Matsumura, Hayahide Yamagishi, Mamoru Komachi
Abstract
Encoder-decoder models typically only employ words that are frequently used in the training corpus because of the computational costs and/or to exclude noisy words. However, this vocabulary set may still include words that interfere with learning in encoder-decoder models. This paper proposes a method for selecting more suitable words for learning encoders by utilizing not only frequency, but also co-occurrence information, which we capture using the HITS algorithm. The proposed method is applied to two tasks: machine translation and grammatical error correction. For Japanese-to-English translation, this method achieved a BLEU score that was 0.56 points more than that of a baseline. It also outperformed the baseline method for English grammatical error correction, with an F-measure that was 1.48 points higher.- Anthology ID:
- P18-3016
- Volume:
- Proceedings of ACL 2018, Student Research Workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 112–119
- Language:
- URL:
- https://aclanthology.org/P18-3016
- DOI:
- 10.18653/v1/P18-3016
- Cite (ACL):
- Satoru Katsumata, Yukio Matsumura, Hayahide Yamagishi, and Mamoru Komachi. 2018. Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models. In Proceedings of ACL 2018, Student Research Workshop, pages 112–119, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models (Katsumata et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P18-3016.pdf
- Code
- Katsumata420/HITS_Ranking