Parallel corpus is an indispensable resource for translation model training in statistical machine translation ( SMT ) . Instead of collecting more and more parallel training corpora , this paper aims to improve SMT performance by exploiting full potential of the existing parallel corpora . Two kinds of methods are proposed : offline data optimization and online model optimization . The offline method adapts the training data by redistributing the weight of each training sentence pairs . The online method adapts the translation model by redistributing the weight of each predefined submodels . Information retrieval model is used for the weighting scheme in both methods . Experimental results show that without using any additional resource , both methods can improve SMT performance significantly . Statistical machine translation relies heavily on the available training data . Typically , the more data is used to estimate the parameters of the translation model , the better it can approximate the true translation probabilities , which will obviously lead to a higher translation performance . However , large corpora are not easily available . The collected corpora are usually from very different areas . For example , the parallel corpora provided by LDC come from quite different domains , such as Hongkong laws , Hangkong Hansards and Hongkong news . This results in the problem that a translation system trained on data from a particular domain(e.g . Hongkong Hansards ) will perform poorly when translating text from a different domain(e.g . news articles ) . Our experiments also show that simply putting all these domain specific corpora together will not always improve translation quality . From another aspect , larger amount of training data also requires larger computational resources . With the increasing of training data , the improvement of translation quality will become smaller and smaller . Therefore , while keeping collecting more and more parallel corpora , it is also important to seek effective ways of making better use of available parallel training data . There are two cases when we train a SMT system . In one case , we know the target test set or target test domain , for example , when building a specific domain SMT system or when participating the NIST MT evaluation1 . In the other case , we are unaware of any information of the testing data . This paper presents two methods to exploit full potential of the available parallel corpora in the two cases . For the first case , we try to optimize the training data offline to make it match the test data better in domain , topic and style , thus improving the translation performance . For the second case , we first divide the training data into several domains and train submodels for each domain . Then , in the translation process , we try to optimize the predefined models according to the online input source sentence . Information retrieval model is used for similar sentences retrieval in both methods . Our preliminary experiments show that both methods can improve SMT performance without using any additional data . The remainder of this paper is organized as follows : Section 2 describes the offline data selection and optimization method . Section 3 describes the online model optimization method . The evaluation and discussion are given in section 4 . Related work is introduced before concluding . This paper presents two new methods to improve statistical machine translation performance by making better use of the available parallel training corpora . The offline data selection method adapts the training corpora to the test domain by retrieving similar sentence pairs and redistributing their weight in the training data . Experimental results show that the selected small subset achieves comparable or even better performance than the baseline system with much less training data . The optimized training data can further improve translation performance without using any additional resource . The online model optimization method adapts the translation model to the online test sentence by redistributing the weight of each predefined submodels . Preliminary results show the effectiveness of the method . Our work also demonstrates that in addition to larger training data , more relevant training data is also important for SMT model training . In future work , we will improve our methods in several aspects . Currently , the similar sentence retrieval model and the weighting schemes are very simple . It might work better by trying other sophisticated similarity measure models or using some optimization algorithms to determine submodel 's weights . Introducing language model optimization into our system might further improve translation performance .

Challenge: Statistical machine translation systems require corpora limited in domain and size, and a model trained on one domain does not perform well on other domains.
Approach: They propose offline and online methods to maximize the potential of available corpora by weighting training samples or submodules using an information retrieval model.
Outcome: The proposed approaches improve translation quality without additional resources using even less data, further experiments with larger training data show that the methods can scale.