Beyond manual and automated post-editing, we describe an approach that takes post-editing information to automatically improve the underlying rules and lexical entries of a transfer-based Machine Translation (MT) system. This process can be divided into two main steps. In the first step, an online post-editing tool allows for easy error diagnosis and implicit error categorization. In the second step, an Automatic Rule Refiner performs error remediation, by tracking errors and suggesting repairs that are mostly lexical and morphosyntactic in nature (such as word-order or incorrect agreement in transfer rules). This approach directly improves the intelligibility of corrected MT output and, more interestingly, it generalizes over unseen data, providing improved MT output for similar sentences that have not been corrected. Hence our approach is an alternative to fully-automated Post-Editing.
Available at: http://works.bepress.com/jaime_carbonell/21/