Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence

Md Arafat Sultan, Steven Bethard, Tamara Sumner


Abstract
We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88–92%, demonstrating 1–3% absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.
Anthology ID:
Q14-1018
Volume:
Transactions of the Association for Computational Linguistics, Volume 2
Month:
Year:
2014
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
219–230
Language:
URL:
https://www.aclweb.org/anthology/Q14-1018
DOI:
10.1162/tacl_a_00178
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PDF:
http://aclanthology.lst.uni-saarland.de/Q14-1018.pdf