A Family of Latent Variable Convex Relaxations for IBM Model 2


  • Andrei Simion Columbia University
  • Michael Collins Columbia University
  • Cliff Stein Columbia University




convex optimization, machine learning, NLP, word alignment


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.




How to Cite

Simion, A., Collins, M., & Stein, C. (2015). A Family of Latent Variable Convex Relaxations for IBM Model 2. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9514