Abstract
Defining the reordering search space is a crucial issue in phrase-based SMT between distant languages. In fact, the optimal trade-off between accuracy and complexity of decoding is nowadays reached by harshly limiting the input permutation space. We propose a method to dynamically shape such space and, thus, capture long-range word movements without hurting translation quality nor decoding time. The space defined by loose reordering constraints is dynamically pruned through a binary classifier that predicts whether a given input word should be translated right after another. The integration of this model into a phrase-based decoder improves a strong Arabic-English baseline already including state-of-the-art early distortion cost (Moore and Quirk, 2007) and hierarchical phrase orientation models (Galley and Manning, 2008). Significant improvements in the reordering of verbs are achieved by a system that is notably faster than the baseline, while bleu and meteor remain stable, or even increase, at a very high distortion limit.- Anthology ID:
- Q13-1027
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 1
- Month:
- Year:
- 2013
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 327–340
- Language:
- URL:
- https://aclanthology.org/Q13-1027
- DOI:
- 10.1162/tacl_a_00231
- Cite (ACL):
- Arianna Bisazza and Marcello Federico. 2013. Dynamically Shaping the Reordering Search Space of Phrase-Based Statistical Machine Translation. Transactions of the Association for Computational Linguistics, 1:327–340.
- Cite (Informal):
- Dynamically Shaping the Reordering Search Space of Phrase-Based Statistical Machine Translation (Bisazza & Federico, TACL 2013)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/Q13-1027.pdf