A Family of Latent Variable Convex Relaxations for IBM Model 2
Abstract
Recently, a new convex formulation of IBM Model 2 was introduced. In this paper we develop the theory further and introduce a class of convex relaxations for latent variable models which include IBM Model 2. When applied to IBM Model 2, our relaxation class subsumes the previous relaxation as a special case. As proof of concept, we study a new relaxation of IBM Model 2 which is simpler than the previous algorithm: the new relaxation relies on the use of nothing more than a multinomial EM algorithm, does not require the tuning of a learning rate, and has some favorable comparisons to IBM Model 2 in terms of F-Measure. The ideas presented could be applied to a wide range of NLP and machine learning problems.
Cite
Text
Simion et al. "A Family of Latent Variable Convex Relaxations for IBM Model 2." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9514Markdown
[Simion et al. "A Family of Latent Variable Convex Relaxations for IBM Model 2." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/simion2015aaai-family/) doi:10.1609/AAAI.V29I1.9514BibTeX
@inproceedings{simion2015aaai-family,
title = {{A Family of Latent Variable Convex Relaxations for IBM Model 2}},
author = {Simion, Andrei Arsene and Collins, Michael and Stein, Cliff},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {2318-2324},
doi = {10.1609/AAAI.V29I1.9514},
url = {https://mlanthology.org/aaai/2015/simion2015aaai-family/}
}