Improving Alignments for Better Confusion Networks for Combining Machine Translation Systems

The state-of-the-art system combination method for machine translation (MT) is the word-based combination using confusion networks. One of the crucial steps in confusion network decoding is the alignment of different hypotheses to each other when building a network. In this paper, we present new methods to improve alignment of hypotheses using word synonyms and a two-pass alignment strategy. We demonstrate that combination with the new alignment technique yields up to 2.9 BLEU point improvement over the best input sys

• Type: Conference
• Year: 2008
• Where: COLING
• Authors: Necip Fazil Ayan, Jing Zheng, Wen Wang
• Keywords: COLING 2008, Computational Linguistics, Confusion Network, Two-pass Alignment Strategy, Word-based Combination