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arXiv · 1708.09151

Paradigm Completion for Derivational Morphology

Ryan Cotterell·Ekaterina Vylomova·Huda Khayrallah·Christo Kirov·David Yarowsky
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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 a 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.

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Ryan Cotterell, Ekaterina Vylomova, Huda Khayrallah, Christo Kirov, David Yarowsky. 2017-08-30. Paradigm Completion for Derivational Morphology. https://arxiv.org/abs/1708.09151

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