Incremental HMM Alignment for MT System Combination

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 949–957, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Incremental HMM Alignment for MT System Combination Chi-Ho Li Microsoft Research Asia 49 Zhichun Road, Beijing, China chl@microsoft.com Xiaodong He Microsoft Research One Microsoft Way, Redmond, USA xiaohe@microsoft.com Yupeng Liu Harbin Institute of Technology 92 Xidazhi Street, Harbin, China ypliu@mtlab.hit.edu.cn Ning Xi Nanjing University 8 Hankou Road, Nanjing, China xin@nlp.nju.edu.cn Abstract Inspired by the incremental TER align- ment, we re-designed the Indirect HMM (IHMM) alignment, which is one of the best hypothesis alignment methods for conventional MT system combination, in an incremental manner. One crucial prob- lem of incremental alignment is to align a hypothesis to a confusion network (CN). Our incremental IHMM alignment is im- plemented in three different ways: 1) treat CN spans as HMM states and define state transition as distortion over covered n- grams between two spans; 2) treat CN spans as HMM states and define state tran- sition as distortion over words in compo- nent translations in the CN; and 3) use a consensus decoding algorithm over one hypothesis and multiple IHMMs, each of which corresponds to a component trans- lation in the CN. All these three ap- proaches of incremental alignment based on IHMM are shown to be superior to both incremental TER alignment and conven- tional IHMM alignment in the setting of the Chinese-to-English track of the 2008 NIST Open MT evaluation. 1 Introduction Word-level combination using confusion network (Matusov et al. (2006) and Rosti et al. (2007)) is a widely adopted approach for combining Machine Translation (MT) systems’ output. Word align- ment between a backbone (or skeleton) translation and a hypothesis translation is a key problem in this approach. Translation Edit Rate (TER, Snover et al. (2006)) based alignment proposed in Sim et al. (2007) is often taken as the baseline, and a couple of other approaches, such as the Indi- rect Hidden Markov Model (IHMM, He et al. (2008)) and the ITG-based alignment (Karakos et al. (2008)), were recently proposed with better re- sults reported. With an alignment method, each hypothesis is aligned against the backbone and all the alignments are then used to build a confusion network (CN) for generating a better translation. However, as pointed out by Rosti et al. (2008), such a pair-wise alignment strategy will produce a low-quality CN if there are errors in the align- ment of any of the hypotheses, no matter how good the alignments of other hypotheses are. For ex- ample, suppose we have the backbone “he buys a computer” and two hypotheses “he bought a lap- top computer” and “he buys a laptop”. It will be natural for most alignment methods to produce the alignments in Figure 1a. The alignment of hypoth- esis 2 against the backbone cannot be considered an error if we consider only these two translations; nevertheless, when added with the alignment of another hypothesis, it produces the low-quality CN in Figure …

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