A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 941–948, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination Boxing Chen*, Min Zhang, Haizhou Li and Aiti Aw Institute for Infocomm Research 1 Fusionopolis Way, 138632 Singapore {bxchen, mzhang, hli, aaiti}@i2r.a-star.edu.sg Abstract Recently confusion network decoding shows the best performance in combining outputs from multiple machine translation (MT) sys- tems. However, overcoming different word orders presented in multiple MT systems dur- ing hypothesis alignment still remains the biggest challenge to confusion network-based MT system combination. In this paper, we compare four commonly used word align- ment methods, namely GIZA++, TER, CLA and IHMM, for hypothesis alignment. Then we propose a method to build the confusion network from intersection word alignment, which utilizes both direct and inverse word alignment between the backbone and hypo- thesis to improve the reliability of hypothesis alignment. Experimental results demonstrate that the intersection word alignment yields consistent performance improvement for all four word alignment methods on both Chi- nese-to-English spoken and written language tasks. 1 Introduction Machine translation (MT) system combination technique leverages on multiple MT systems to achieve better performance by combining their outputs. Confusion network based system com- bination for machine translation has shown promising advantage compared with other tech- niques based system combination, such as sen- tence level hypothesis selection by voting and source sentence re-decoding using the phrases or translation models that are learned from the source sentences and target hypotheses pairs (Rosti et al., 2007a; Huang and Papineni, 2007). In general, the confusion network based sys- tem combination method for MT consists of four steps: 1) Backbone selection: to select a back- bone (also called “skeleton”) from all hypotheses. The backbone defines the word orders of the fi- nal translation. 2) Hypothesis alignment: to build word-alignment between backbone and each hy- pothesis. 3) Confusion network construction: to build a confusion network based on hypothesis alignments. 4) Confusion network decoding: to decode the best translation from a confusion network. Among the four steps, the hypothesis alignment presents the biggest challenge to the method due to the varying word orders between outputs from different MT systems (Rosti et al, 2007). Many techniques have been studied to address this issue. Bangalore et al. (2001) used the edit distance alignment algorithm which is extended to multiple strings to build confusion network, it only allows monotonic alignment. Jayaraman and Lavie (2005) proposed a heuris- tic-based matching algorithm which allows non- monotonic alignments to align the words be- tween the hypotheses. More recently, Matusov et al. (2006, 2008) used GIZA++ to produce word alignment for hypotheses pairs. Sim et al. (2007), Rosti et al. (2007a), and Rosti et al. (2007b) used minimum Translation Error Rate (TER) (Snover et al., 2006) alignment to build the confusion network. Rosti et al. (2008) extended TER algo- rithm which allows a confusion network as the reference to compute word …

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