This paper presents an study of the use of interlocking phrases in phrase-based statistical machine translation . We examine the effect on translation quality when the translation units used in the translation hypotheses are allowed to overlap on the source side , on the target side and on both sides . A large-scale evaluation on 380 language pairs was conducted . Our results show that overall the use of overlapping phrases improved translation quality by 0.3 BLEU points on average . Further analysis revealed that language pairs requiring a larger amount of re-ordering benefited the most from our approach . When the evaluation was restricted to such pairs , the average improvement increased to up to 0.75 BLEU points with over 97 % of the pairs improving . Our approach requires only a simple modification to the decoding algorithm and we believe it should be generally applicable to improve the performance of phrase-based decoders . In this paper we examine the effect on machine translation quality of using interlocking phrases to during the decoding process in phrase-based statistical machine translation ( PBSMT ) . The motivation for this is two-fold . Firstly , during the phrase-pair extraction process that occurs in the training of a typical PBSMT system , all possible alternative phrase-pairs are extracted that are consistent with a set of alignment points . As a consequence , the source and target sides of these extracted phrase pairs may over-lap . However , in contrast to this , the decoding process traditionally proceeds by concatenating disjoint translation units ; the process relies on the language model to eliminate awkward hypotheses with repeated words produced by sequences of translation units that overlap . Secondly , the transduction process in PBSMT is carried out by generating hypotheses that are composed of sequences of translation units . These sequences are normally generated independently , as modeling the dependencies between them is difficult due to the data sparseness issues arising from modeling with word sequences . The process of interlocking is a way of introducing a form of dependency between translation units , effectively producing larger units from pairs of compatible units . 2 Related Work ( Karimova et al . , 2014 ) presented a method to extract overlapping phrases offline for hierarchical phrase based SMT . They used the CDEC SMT decoder ( Dyer et al . , 2010 ) that offers several learners for discriminative tuning of weights for the new phrases . Their results showed improvements of 0.3 to 0.6 BLEU points over discriminatively trained hierarchical phrase-based SMT systems on two datasets for German-to-English translation . ( Tribble and et al . , 2003 ) proposed a method to generate longer new phrases by merging existing phraselevel alignments that have overlaping words on both source and target sides . Their experiments on translating Arabic-English text from the news domain were encouraging . ( Roth and McCallum , 2010 ) proposed a conditional-random-field approach to discriminatively train phrase based machine translation in which training and decoding are both cast in a sampling framework . Different with traditional PB-SMT decoding that infers both a Viterbi alignment and the target sentence , their approach produced a rich overlapping phrase alignment . Their approach leveraged arbitrary features of the entire source sentence , target sentence and alignment . ( Kääriäinen , 2009 ) proposed a novel phrase-based conditional exponential family translation model for SMT . The model operates on a feature representation in which sentence level translations are represented by enumerating all the known phrase level translations that occur inside them . The model automatically takes into account information provided by phrase overlaps . Although both of the latter two approaches were innovative the translation performance was lower than tranditional PBSMT baselines . Our proposed approach is most similar to that of ( Tribble and et al . , 2003 ) . Our approach differs in the interlocking process is less constrained ; phrase pairs can interlock independently on source and target sides , and the interlocking process performed during the decoding process itself , rather than by augmenting the phrase-table . In this paper we propose and evaluate a simple technique for improving the performance of phrasebased statistical machine translation decoders , that can be implemented with only minor modifications to the decoder . In the proposed method phrases are allowed to interlock freely on both the source and target side during decoding . The experimental results , based on a large-scale study involving 380 language pairs provide strong evidence that our approach is genuinely effective in improving the machine translation quality . The translation quality improved for 77 % of the language pairs tested , and this was increased to over 97 % when the set of language pairs was filtered according to Kendall 's tau distance . The translation quality improved by an average of up to 0.75 BLEU points on this subset . This value represents a lower bound on what is possible with this technique and in future work we intend to study the introduction of additional features into the log-linear model to encourage or discourage the use of interlocking phrases during decoding , and investigate the effect of increasing the number of interlocked words .

Challenge: Existing phrase-based statistical machine translation systems can generate the same words repeatedly because there can be overlaps in extracted phrases.
Approach: They first analyze the effect of overlapping phrases and propose a simple modification to the decoder to handle the interlocking phrases.
Outcome: A large-scale evaluation including 380 language pairs reveals that their modification effectively improves the translation quality.