Current system combination methods usually use confusion networks to find consensus translations among different systems . Requiring one-to-one mappings between the words in candidate translations , confusion networks have difficulty in handling more general situations in which several words are connected to another several words . Instead , we propose a lattice-based system combination model that allows for such phrase alignments and uses lattices to encode all candidate translations . Experiments show that our approach achieves significant improvements over the state-ofthe-art baseline system on Chinese-to-English translation test sets . System combination aims to find consensus translations among different machine translation systems . It has been proven that such consensus translations are usually better than the output of individual systems ( Frederking and Nirenburg , 1994 ) . In recent several years , the system combination methods based on confusion networks developed rapidly ( Bangalore et al . , 2001 ; Matusov et al . , 2006 ; Sim et al . , 2007 ; Rosti et al . , 2007a ; Rosti et al . , 2007b ; Rosti et al . , 2008 ; He et al . , 2008 ) , which show state-of-the-art performance in benchmarks . A confusion network consists of a sequence of sets of candidate words . Each candidate word is associated with a score . The optimal consensus translation can be obtained by selecting one word from each set to maximizing the overall score . To construct a confusion network , one first need to choose one of the hypotheses ( i.e. , candidate translations ) as the backbone ( also called " skeleton " in the literature ) and then decide the word alignments of other hypotheses to the backbone . Hypothesis alignment plays a crucial role in confusionnetwork-based system combination because it has a direct effect on selecting consensus translations . However , a confusion network is restricted in such a way that only 1-to-1 mappings are allowed in hypothesis alignment . This is not the fact even for word alignments between the same languages . It is more common that several words are connected to another several words . For example , " be capable of " and " be able to " have the same meaning . Although confusion-network-based approaches resort to inserting null words to alleviate this problem , they face the risk of producing degenerate translations such as " be capable to " and " be able of " . In this paper , we propose a new system combination method based on lattices . As a more general form of confusion network , a lattice is capable of describing arbitrary mappings in hypothesis alignment . In a lattice , each edge is associated with a sequence of words rather than a single word . Therefore , we select phrases instead of words in each candidate set and minimize the chance to produce unexpected translations such as " be capable to " . We compared our approach with the state-of-the-art confusion-network-based system ( He et al . , 2008 ) and achieved a significant absolute improvement of 1.23 BLEU points on the NIST 2005 Chinese-to-English test set and 0.93 BLEU point on the NIST 2008 Chinese-to-English test set . The alignment model plays an important role in system combination . Because of the expression limitation of confusion networks , only 1-to-1 mappings are employed in the confusion-network-based model . This paper proposes a lattice-based system combination model . As a general form of confusion networks , lattices can express n-to-n mappings . a confusion-network-based model processes words only . As a result , phrase pairs must be extracted before constructing a lattice . On NIST MT05 test set , the lattice-based system gave better results with an absolute improvement of 1.23 BLEU points over the confusionnetwork-based system ( He et al . , 2008 ) and 3.73 BLEU points over the best single system . On NIST MT08 test set , the lattice-based system outperformed the confusion-network-based system by 0.93 BLEU point and outperformed the best single system by 3.0 BLEU points .
System combination methods based on confusion networks only allow word level 1-to-1 mappings, and some workarounds cause another type of problem such as degeneration.
They propose to use lattices to combine systems that enable to process of a sequence of words rather than one word that can mitigate degeneration.
They show that their approach significantly outperforms the state-of-the-art confusion-network-based systems on Chinese-to-English translation tasks.