Paradigm Completion for Derivational Morphology

Ryan Cotterell, Ekaterina Vylomova, Huda Khayrallah, Christo Kirov, David Yarowsky


Abstract
The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.
Anthology ID:
D17-1074
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
714–720
Language:
URL:
https://aclanthology.org/D17-1074
DOI:
10.18653/v1/D17-1074
Bibkey:
Cite (ACL):
Ryan Cotterell, Ekaterina Vylomova, Huda Khayrallah, Christo Kirov, and David Yarowsky. 2017. Paradigm Completion for Derivational Morphology. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 714–720, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Paradigm Completion for Derivational Morphology (Cotterell et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1074.pdf
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