Koehn et al, ACL 2003

Being edited by Rui Correia

Citation

Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase-based translation. In Proceedings of HLT-NAACL 2003, pages 127–133. [1]

Summary

In this paper the authors propose a new framework that aims at explaining and understanding why phrase-based models in Machine Translation outperform word-based models.

Within this framework (phrase-based translation model and decoding algorithm) the authors carry experiments that explore three different methods for learning phrase translation (based on word alignments, on syntactic information and "pure" phrase alignments). Additionally the authors also explore phrase length, lexical weighting, and the impact of different language pairs in the overall BLEU score.

The results confirm the already proved hypotheses that phrase translation achieve better performance than word-based methods, adding that three-word phrase are sufficient to outperform the traditional methods. Moreover, the authors conclude that lexical weighting of phrase translation boost results, and that syntactic considerations, on the other hand, hinder the results.

Evaluation Framework

The phrase translation model used in the proposed framework is based on the noisy channel model. The best English output sentence $e_{best}$ given a foreign input sentence $f$ is given by:

$e_{best}=\arg \max _{e}p(e|f)=\arg \max _{e}p(f|e)p_{LM}(e)\omega ^{length(e)}$

Methods for Learning Phrase Translation

Vogal et al, COLING 1996

$\max _{0\leq z\leq 1}\sum _{jk\in \varepsilon }{s_{jk}z_{jk}}+\sum _{jklm\in Q}{s_{jklm}z_{jklm}}$

      $s.t.\sum _{j\in V^{s}}z_{jk}\leq 1,\forall k\in V^{t};$

          $\sum _{k\in V^{t}}z_{jk}\leq 1,\forall j\in V^{s};$

          $z_{jklm}\leq z_{jk},z_{jklm}\leq z_{lm},\forall jklm\in Q,$

Experimental Results

The authors used the Hansards