Abstract
To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segment-level phonotactic acquisition, we compare the performance of two recurrent neural network models of phonotactic learning: one that has access to distinctive features at the start of the learning process, and one that does not. Though the predictions of both models are significantly correlated with human judgments of non-words, the feature-naive model significantly outperforms the feature-aware one in terms of probability assigned to a held-out test set of English words, suggesting that distinctive features are not obligatory for learning phonotactic patterns at the segment level.- Anthology ID:
- P19-1155
- Erratum e1:
- P19-1155e1
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1595–1605
- Language:
- URL:
- https://aclanthology.org/P19-1155
- DOI:
- 10.18653/v1/P19-1155
- Cite (ACL):
- Nicole Mirea and Klinton Bicknell. 2019. Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1595–1605, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning (Mirea & Bicknell, ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P19-1155.pdf