Abstract
Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ambiguities of inflected words in order to map them to a dictionary headword. In the present paper we aim to improve lemmatization performance on a set of non-standard historical languages in which the difficulty is increased by an additional aspect (iii): spelling variation due to lacking orthographic standards. We approach lemmatization as a string-transduction task with an Encoder-Decoder architecture which we enrich with sentence information using a hierarchical sentence encoder. We show significant improvements over the state-of-the-art by fine-tuning the sentence encodings to jointly optimize a bidirectional language model loss. Crucially, our architecture does not require POS or morphological annotations, which are not always available for historical corpora. Additionally, we also test the proposed model on a set of typologically diverse standard languages showing results on par or better than a model without fine-tuned sentence representations and previous state-of-the-art systems. Finally, to encourage future work on processing of non-standard varieties, we release the dataset of non-standard languages underlying the present study, which is based on openly accessible sources.- Anthology ID:
- N19-1153
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1493–1503
- Language:
- URL:
- https://aclanthology.org/N19-1153
- DOI:
- 10.18653/v1/N19-1153
- Cite (ACL):
- Enrique Manjavacas, Ákos Kádár, and Mike Kestemont. 2019. Improving Lemmatization of Non-Standard Languages with Joint Learning. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1493–1503, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Improving Lemmatization of Non-Standard Languages with Joint Learning (Manjavacas et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/N19-1153.pdf
- Code
- emanjavacas/pie + additional community code
- Data
- Universal Dependencies