Most statistical translation systems are based on phrase translation pairs , or " blocks " , which are obtained mainly from word alignment . We use blocks to infer better word alignment and improved word alignment which , in turn , leads to better inference of blocks . We propose two new probabilistic models based on the innerouter segmentations and use EM algorithms for estimating the models ' parameters . The first model recovers IBM Model-1 as a special case . Both models outperform bidirectional IBM Model-4 in terms of word alignment accuracy by 10 % absolute on the F-measure . Using blocks obtained from the models in actual translation systems yields statistically significant improvements in Chinese-English SMT evaluation . Today 's statistical machine translation systems rely on high quality phrase translation pairs to acquire state-of-the-art performance , see ( Koehn et al . , 2003 ; Zens and Ney , 2004 ; Och and Ney , 2003 ) . Here , phrase pairs , or " blocks " are obtained automatically from parallel sentence pairs via the underlying word alignments . Word alignments traditionally are based on IBM Models 1 - 5 ( Brown et al . , 1993 ) or on HMMs ( Vogel et al . , 1996 ) . Automatic word alignment is challenging in that its accuracy is not yet close to inter-annotator agreement in some language pairs : for Chinese-English , inter-annotator agreement exceeds 90 on F-measure whereas IBM Model-4 or HMM accuracy is typically below 80s . HMMs assume that words " close-in-source " are aligned to words " close-in-target " . While this locality assumption is generally sound , HMMs do have limitations : the self-transition probability of a state ( word ) is the only control on the duration in the state , the length of the phrase aligned to the word . Also there is no natural way to control repeated non-contiguous visits to a state . Despite these problems , HMMs remain attractive for their speed and reasonable accuracy . We propose a new method for localizing word alignments . We use blocks to achieve locality in the following manner : a block in a sentence pair is a source phrase aligned to a target phrase . We assume that words inside the source phrase can not align to words outside the target phrase and that words outside the source phrase can not align to words inside the target phrase . Furthermore , a block divides the sentence pair into two smaller regions : the inner part of the block , which corresponds to the source and target phrase in the block , and the outer part of the block , which corresponds to the remaining source and target words in the parallel sentence pair . The two regions are non-overlapping ; and each of them is shorter than the original parallel sentence pair . The regions are thus easier to align than the original sentence pairs ( e.g. , using IBM Model-1 ) . While the model uses a single block to split the sentence pair into two independent regions , it is not clear which block we should select for this purpose . Therefore , we treat the splitting block as a hidden variable . This proposed approach is far simpler than treating the entire sentence as a sequence of nonoverlapping phrases ( or chunks ) and considering such sequential segmentation either explicitly or implicitly . For example , ( Marcu and Wong , 2002 ) for a joint phrase based model , ( Huang et al . , 2003 ) for a translation memory system ; and ( Watanabe et al . , 2003 ) for a complex model of insertion , deletion and head-word driven chunk reordering . Other approaches including ( Watanabe et al . , 2002 ) treat extracted phrase-pairs as new parallel data with limited success . Typically , they share a similar architecture of phrase level segmentation , reordering , translation as in ( Och and Ney , 2002 ; Koehn and Knight , 2002 ; Yamada and Knight , 2001 ) . The phrase level interaction has to be taken care of for the non-overlapping sequential segmentation in a complicated way . Our models model such interactions in a soft way . The hidden blocks are allowed to overlap with each other , while each block induced two non-overlapping regions , i.e. the model brackets the sentence pair into two independent parts which are generated synchronously . In this respect , it resembles bilingual bracketing ( Wu , 1997 ) , but our model has more lexical items in the blocks with many-to-many word alignment freedom in both inner and outer parts . We present our localization constraints using blocks for word alignment in Section 2 ; we detail our two new probabilistic models and their EM training algorithms in Section 3 ; our baseline system , a maximum-posterior inference for word alignment , is explained in Section 4 ; experimental results of alignments and translations are in Section 5 ; and Section 6 contains discussion and conclusions . Our main contributions are two novel Inner-Outer Bracket models based on segmentations induced by hidden blocks . Modeling the Inner-Outer hidden segmentations , we get significantly improved word alignments for both the small training set and the large training set over the widely-practiced bidirectional IBM Model-4 alignment . We also show significant improvements in translation quality using our proposed bracket models . Robustness to noisy blocks merits further investigation .

Challenge: Automatic word alignment used in statistical machine translations, does not achieve satisfactory performance in some language pairs such as because of the limitations of HMMs.
Approach: They propose to use phrase translation pairs to get better word alignments using two new probabilistic models based on EM-algorithm that localizes the alignments.
Outcome: The proposed models outperform IBM Model-4 by 10%, both on small and large training setups, and the translation models based on this result improve qualities.