Watanabe et al., EMNLP 2007. Online Large-Margin Training for Statistical Machine Translation

Citation

Taro Watanabe, Jun Suzuki, Hajime Tsukada, Hideki Isozaki. 2007. Online large-margin training for statistical machine translation. In Proceedings of EMNLP-CoNLL. pp 764–773

Online Version

Online large-margin training for statistical machine translation

Summary

This paper basically introduces an online discriminative large-margin training approach to statistical machine translation. The authors achieved the then state of the art performance on an Arabic-English translation task by tuning a combination of millions of features in an MT system. By following this approach the authors also addressed the problem of scaling machine translation systems with a large number of features of the order of millions.

Method

The paper presents a method to estimate a large number of parameters — of the order of millions — using an online training algorithm. The algorithm used in this work is the Margin Infused Relaxed Algorithm (MIRA) which has been successfully employed for many structured natural language processing tasks such as, dependency parsing, a joint-labeling/chunking task. This method is applied to an enhanced hierarchical machine translation model.

Hierarchical Phrase-based SMT

Chiang (2005) introduced the hierarchical phrase-based translation approach, in which non-terminals are embedded in each phrase. A translation is generated by hierarchically combining phrases using the non-terminals. Such a quasi-syntactic structure can naturally capture the reordering of phrases that is not directly modeled by a conventional phrase-based approach.

Each production rule in the hierarchical phrase-based translation model is given by: Failed to parse (syntax error): {\displaystyle X &rArr \langle &gamma \bar{b} &beta \tilde \rangle }