Constrained decoding is of great importance not only for speed but also for translation quality . Previous efforts explore soft syntactic constraints which are based on constituent boundaries deduced from parse trees of the source language . We present a new framework to establish soft constraints based on a more natural alternative : translation boundary rather than constituent boundary . We propose simple classifiers to learn translation boundaries for any source sentences . The classifiers are trained directly on word-aligned corpus without using any additional resources . We report the accuracy of our translation boundary classifiers . We show that using constraints based on translation boundaries predicted by our classifiers achieves significant improvements over the baseline on large-scale Chinese-to-English translation experiments . The new constraints also significantly outperform constituent boundary based syntactic constrains . It has been known that phrase-based decoding ( phrase segmentation / translation / reordering ( Chiang , 2005 ) ) should be constrained to some extent not only for transferring the NP-hard problem ( Knight , 1999 ) into a tractable one in practice but also for improving translation quality . For example , Xiong et al . ( 2008 ) find that translation quality can be significantly improved by either prohibiting reorderings around punctuation or restricting reorderings within a 15-word window . Recently , more linguistically motivated constraints are introduced to improve phrase-based decoding . ( Cherry , 2008 ) and ( Marton and Resnik , 2008 ) introduce syntactic constraints into the standard phrase-based decoding ( Koehn et al . , 2003 ) and hierarchical phrase-based decoding ( Chiang , 2005 ) respectively by using a counting feature which accumulates whenever hypotheses violate syntactic boundaries of source-side parse trees . ( Xiong et al . , 2009 ) further presents a bracketing model to include thousands of context-sensitive syntactic constraints . All of these approaches achieve their improvements by guiding the phrase-based decoder to prefer translations which respect source-side parse trees . One major problem with such constituent boundary based constraints is that syntactic structures of the source language do not necessarily reflect translation structures where the source and target language correspond to each other . In this paper , we investigate building classifiers that directly address the problem of translation boundary , rather than extracting constituent boundary from sourceside parsers built for a different purpose . A translation boundary is a position in the source sequence which begins or ends a translation zone1 spanning multiple source words . In a translation zone , the source phrase is translated as a unit . Reorderings which cross translation zones are not desirable . Inspired by ( Roark and Hollingshead , 2008 ) which introduces classifiers to decide if a word can begin / end a multi-word constituent , we build two discriminative classifiers to tag each word in the source sequence with a binary class label . The first classifier decides if a word can begin a multi-sourceword translation zone ; the second classifier decides if a word can end a multi-source-word translation zone . Given a partial translation covering source sequence ( i , j ) with start word c i and end word c j2 , this translation can be penalized if the first classifier decides that the start word c i can not be a beginning translation boundary or the second classifier decides that the end word c j can not be an ending translation boundary . In such a way , we can guide the decoder to boost hypotheses that respect translation boundaries and therefore the common translation structure shared by the source and target language , rather than the syntactic structure of the source language . We report the accuracy of such classifiers by comparing their outputs with " gold " translation boundaries obtained from reference translations on the development set . We integrate translation boundary based constraints into phrase-based decoding and display that they improve translation quality significantly in large-scale experiments . Furthermore , we confirm that they also significantly outperform constituent boundary based syntactic constraints . In this paper , we have presented a simple approach to learn translation boundaries on source sentences . The learned translation boundaries are used to constrain phrase-based decoding in a soft manner . The whole approach has several properties . • First , it is based on a simple classification task that can achieve considerably high accuracy when taking translation divergences into account using simple models and features . • Second , the classifier output can be straightforwardly used to constrain phrase-based decoder . • Finally , we have empirically shown that , to build soft constraints for phrase-based decoding , translation boundary predicted by our classifier is a better choice than constituent boundary deduced from source-side parse tree . Future work in this direction will involve trying different methods to define more informative translation boundaries , such as a boundary to begin / end a swapping . We would also like to investigate new methods to incorporate automatically learned translation boundaries more efficiently into decoding in an attempt to further improve search in both speed and accuracy .

Challenge: Existing methods guide phrase-based decoders to prefer translations which respect source-side parse trees however syntactic structures of the source do not necessarily reflect translation structure.
Approach: They propose to use binary classifiers trained on the word-aligned corpus to predict translation boundaries for source sentences and use them as soft constraints.
Outcome: The proposed classifiers outperform ones with access to gold translation boundaries and a phrase-based translation model with the proposed constraints improves translation quality.