What just happened? Evaluating retrofitted distributional word vectors

Dmetri Hayes


Abstract
Recent work has attempted to enhance vector space representations using information from structured semantic resources. This process, dubbed retrofitting (Faruqui et al., 2015), has yielded improvements in word similarity performance. Research has largely focused on the retrofitting algorithm, or on the kind of structured semantic resources used, but little research has explored why some resources perform better than others. We conducted a fine-grained analysis of the original retrofitting process, and found that the utility of different lexical resources for retrofitting depends on two factors: the coverage of the resource and the evaluation metric. Our assessment suggests that the common practice of using correlation measures to evaluate increases in performance against full word similarity benchmarks 1) obscures the benefits offered by smaller resources, and 2) overlooks incremental gains in word similarity performance. We propose root-mean-square error (RMSE) as an alternative evaluation metric, and demonstrate that correlation measures and RMSE sometimes yield opposite conclusions concerning the efficacy of retrofitting. This point is illustrated by word vectors retrofitted with novel treatments of the FrameNet data (Fillmore and Baker, 2010).
Anthology ID:
N19-1111
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1062–1072
Language:
URL:
https://aclanthology.org/N19-1111
DOI:
10.18653/v1/N19-1111
Bibkey:
Cite (ACL):
Dmetri Hayes. 2019. What just happened? Evaluating retrofitted distributional word vectors. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1062–1072, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
What just happened? Evaluating retrofitted distributional word vectors (Hayes, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1111.pdf
Software:
 N19-1111.Software.txt