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 .
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.
They propose offline and online methods to maximize the potential of available corpora by weighting training samples or submodules using an information retrieval model.
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.