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Báo cáo khoa học: "Bridging SMT and TM with Translation Recommendation"

We propose a translation recommendation framework to integrate Statistical Machine Translation (SMT) output with Translation Memory (TM) systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We describe an implementation of this framework using an SVM binary classifier. | Bridging SMT and TM with Translation Recommendation Yifan He Yanjun Ma Josef van Genabith Andy Way Centre for Next Generation Localisation School of Computing Dublin City University yhe yma josef away @computing.dcu.ie Abstract We propose a translation recommendation framework to integrate Statistical Machine Translation SMT output with Translation Memory TM systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We describe an implementation of this framework using an SVM binary classifier. We exploit methods to fine-tune the classifier and investigate a variety of features of different types. We rely on automatic MT evaluation metrics to approximate human judgements in our experiments. Experimental results show that our system can achieve 0.85 precision at 0.89 recall excluding exact matches. Furthermore it is possible for the end-user to achieve a desired balance between precision and recall by adjusting confidence levels. 1 Introduction Recent years have witnessed rapid developments in statistical machine translation SMT with considerable improvements in translation quality. For certain language pairs and applications automated translations are now beginning to be considered acceptable especially in domains where abundant parallel corpora exist. However these advances are being adopted only slowly and somewhat reluctantly in professional localization and post-editing environments. Post-editors have long relied on translation memories TMs as the main technology assisting translation and are understandably reluctant to give them up. There are several simple reasons for this 1 TMs are useful 2 TMs represent considerable effort and investment by a company or even more so an individual translator 3 the fuzzy match score used in TMs offers a good approximation of post-editing effort which is useful both for translators and translation cost estimation and 4 .