Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation

Murhaf Fares, Stephan Oepen, Erik Velldal


Abstract
In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun–noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.
Anthology ID:
D18-1178
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1488–1498
Language:
URL:
https://www.aclweb.org/anthology/D18-1178
DOI:
10.18653/v1/D18-1178
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PDF:
http://aclanthology.lst.uni-saarland.de/D18-1178.pdf