Year: 2010
Type: other
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\nmetrics 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. futhermore, it is possible for the end-user to achieve a desired balance between precision and recall by adjusting\nconfidence levels.
Authors: Yifan He, Yanjun Ma, Josef van Genabith, Andy Way
Institution: Dublin City University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2145790651