An Encoder-Decoder Approach to the Paradigm Cell Filling Problem

Miikka Silfverberg, Mans Hulden


Abstract
The Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones. We implement novel neural models for this task, evaluating them on 18 data sets in 8 languages, showing performance that is comparable with previous work with far less training data. We also publish a new dataset for this task and code implementing the system described in this paper.
Anthology ID:
D18-1315
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2883–2889
Language:
URL:
https://aclanthology.org/D18-1315
DOI:
10.18653/v1/D18-1315
Bibkey:
Cite (ACL):
Miikka Silfverberg and Mans Hulden. 2018. An Encoder-Decoder Approach to the Paradigm Cell Filling Problem. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2883–2889, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
An Encoder-Decoder Approach to the Paradigm Cell Filling Problem (Silfverberg & Hulden, EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/D18-1315.pdf
Video:
 https://vimeo.com/305674091
Code
 mpsilfve/pcfp-data