Abstract
Word identification from continuous input is typically viewed as a segmentation task. Experiments with human adults suggest that familiarity with syntactic structures in their native language also influences word identification in artificial languages; however, the relation between syntactic processing and word identification is yet unclear. This work takes one step forward by exploring a radically different approach of word identification, in which segmentation of a continuous input is viewed as a process isomorphic to unsupervised constituency parsing. Besides formalizing the approach, this study reports simulations of human experiments with DIORA (Drozdov et al., 2020), a neural unsupervised constituency parser. Results show that this model can reproduce human behavior in word identification experiments, suggesting that this is a viable approach to study word identification and its relation to syntactic processing.- Anthology ID:
- 2022.acl-long.283
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4103–4112
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.283
- DOI:
- 10.18653/v1/2022.acl-long.283
- Cite (ACL):
- Raquel G. Alhama. 2022. Word Segmentation as Unsupervised Constituency Parsing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4103–4112, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Word Segmentation as Unsupervised Constituency Parsing (Alhama, ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.acl-long.283.pdf
- Data
- OpenSubtitles