For resource-limited language pairs , coverage of the test set by the parallel corpus is an important factor that affects translation quality in two respects : 1 ) out of vocabulary words ; 2 ) the same information in an input sentence can be expressed in different ways , while current phrase-based SMT systems can not automatically select an alternative way to transfer the same information . Therefore , given limited data , in order to facilitate translation from the input side , this paper proposes a novel method to reduce the translation difficulty using source-side lattice-based paraphrases . We utilise the original phrases from the input sentence and the corresponding paraphrases to build a lattice with estimated weights for each edge to improve translation quality . Compared to the baseline system , our method achieves relative improvements of 7.07 % , 6.78 % and 3.63 % in terms of BLEU score on small , medium and largescale English-to-Chinese translation tasks respectively . The results show that the proposed method is effective not only for resourcelimited language pairs , but also for resourcesufficient pairs to some extent . In recent years , statistical MT systems have been easy to develop due to the rapid explosion in data availability , especially parallel data . However , in reality there are still many language pairs which lack parallel data , such as Urdu-English , Chinese-Italian , where large amounts of speakers exist for both languages ; of course , the problem is far worse for pairs such as Catalan-Irish . For such resourcelimited language pairs , sparse amounts of parallel data would cause the word alignment to be inaccurate , which would in turn lead to an inaccurate phrase alignment , and bad translations would result . Callison-Burch et al . ( 2006 ) argue that limited amounts of parallel training data can lead to the problem of low coverage in that many phrases encountered at run-time are not observed in the training data and so their translations will not be learned . Thus , in recent years , research on addressing the problem of unknown words or phrases has become more and more evident for resource-limited language pairs . Callison-Burch et al . ( 2006 ) proposed a novel method which substitutes a paraphrase for an unknown source word or phrase in the input sentence , and then proceeds to use the translation of that paraphrase in the production of the target-language result . Their experiments showed that by translating paraphrases a marked improvement was achieved in coverage and translation quality , especially in the case of unknown words which previously had been left untranslated . However , on a large-scale data set , they did not achieve improvements in terms of automatic evaluation . Nakov ( 2008 ) proposed another way to use paraphrases in SMT . He generates nearly-equivalent syntactic paraphrases of the source-side training sentences , then pairs each paraphrased sentence with the target translation associated with the original sentence in the training data . Essentially , this method generates new training data using paraphrases to train a new model and obtain more useful phrase pairs . However , he reported that this method results in bad system performance . By contrast , real improvements can be achieved by merging the phrase tables of the paraphrase model and the original model , giving priority to the latter . Schroeder et al . ( 2009 ) presented the use of word lattices for multi-source translation , in which the multiple source input texts are compiled into a compact lattice , over which a single decoding pass is then performed . This lattice-based method achieved positive results across all data conditions . In this paper , we propose a novel method using paraphrases to facilitate translation , especially for resource-limited languages . Our method does not distinguish unknown words in the input sentence , but uses paraphrases of all possible words and phrases in the source input sentence to build a source-side lattice to provide a diverse and flexible list of source-side candidates to the SMT decoder so that it can search for a best path and deliver the translation with the highest probability . In this case , we neither need to change the phrase table , nor add new features in the log-linear model , nor add new sentences in the training data . The remainder of this paper is organised as follows . In Section 2 , we define the " translation difficulty " from the perspective of the source side , and then examine how well the test set is covered by the phrase table and the parallel training data . Section 3 describes our paraphrase lattice method and discusses how to set the weights for the edges in the lattice network . In Section 4 , we report comparative experiments conducted on small , medium and largescale English-to-Chinese data sets . In Section 5 , we analyse the influence of our paraphrase lattice method . Section 6 concludes and gives avenues for future work . 2 What Makes Translation Difficult ? In this paper , we proposed a novel method using paraphrase lattices to facilitate the translation process in SMT . Given an input sentence , our method firstly discovers all possible paraphrases from a paraphrase database for N -grams ( 1 < = N < = 10 ) in the test set , and then filters out the paraphrases which do not appear in the phrase table in order to avoid adding new unknown words on the input side . We then use the original words and the paraphrases to build a word lattice , and set the weights to prioritise the original edges and penalise the paraphrase edges . Finally , we import the lattice into the decoder to perform lattice decoding . The experiments are conducted on English-to-Chinese translation using the FBIS data set with small and medium-sized amounts of data , and on a large-scale corpus of 2.1 million sentence pairs . We also performed comparative experiments for the baseline , the " Para-Sub " system and our paraphrase lattice-based system . The experimental results show that our proposed system significantly outperforms the baseline and the " Para-Sub " system , and the effectiveness is consistent on the small , medium and large-scale data sets . As for future work , firstly we plan to propose a pruning algorithm for the duplicate paths in the lattice , which will track the edge generation with respect to the path span , and thus eliminate duplicate paths . Secondly , we plan to experiment with another feature function in the log-linear model to discount words derived from paraphrases , and use MERT to assign an appropriate weight to this feature function .

Challenge: Vocabulary coverage is crucial for SMT systems, especially for resource-lean language pairs, however; current systems cannot find alternative solutions.
Approach: They propose to use paraphrasing the source side of inputs at test time listing all the possible alternatives to expand the lattice.
Outcome: They show that their system outperforms the baseline system in the English-Chinese translation task for both resource-lean and sufficient setups.