Abstract
This paper presents DeepSPIN’s submissions to the SIGMORPHON 2022 Shared Task on Morpheme Segmentation. We make three submissions, all to the word-level subtask. First, we show that entmax-based sparse sequence-tosequence models deliver large improvements over conventional softmax-based models, echoing results from other tasks. Then, we challenge the assumption that models for morphological tasks should be trained at the character level by building a transformer that generates morphemes as sequences of unigram language model-induced subwords. This subword transformer outperforms all of our character-level models and wins the word-level subtask. Although we do not submit an official submission to the sentence-level subtask, we show that this subword-based approach is highly effective there as well.- Anthology ID:
- 2022.sigmorphon-1.14
- Volume:
- Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Editors:
- Garrett Nicolai, Eleanor Chodroff
- Venue:
- SIGMORPHON
- SIG:
- SIGMORPHON
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 131–138
- Language:
- URL:
- https://aclanthology.org/2022.sigmorphon-1.14
- DOI:
- 10.18653/v1/2022.sigmorphon-1.14
- Cite (ACL):
- Ben Peters and Andre F. T. Martins. 2022. Beyond Characters: Subword-level Morpheme Segmentation. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 131–138, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- Beyond Characters: Subword-level Morpheme Segmentation (Peters & Martins, SIGMORPHON 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.sigmorphon-1.14.pdf
- Data
- UniMorph 4.0