A Distributional and Orthographic Aggregation Model for English Derivational Morphology

Daniel Deutsch, John Hewitt, Dan Roth


Abstract
Modeling derivational morphology to generate words with particular semantics is useful in many text generation tasks, such as machine translation or abstractive question answering. In this work, we tackle the task of derived word generation. That is, we attempt to generate the word “runner” for “someone who runs.” We identify two key problems in generating derived words from root words and transformations. We contribute a novel aggregation model of derived word generation that learns derivational transformations both as orthographic functions using sequence-to-sequence models and as functions in distributional word embedding space. The model then learns to choose between the hypothesis of each system. We also present two ways of incorporating corpus information into derived word generation.
Anthology ID:
P18-1180
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1938–1947
Language:
URL:
https://www.aclweb.org/anthology/P18-1180
DOI:
10.18653/v1/P18-1180
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-1180.pdf
Video:
 https://vimeo.com/288152732
Presentation:
 P18-1180.Presentation.pdf