Joke Daems

The Effectiveness of Consulting External Resources During Translation and Post-editing of General Text Types

Consulting external resources is an important aspect of the translation process. Whereas most previous studies were limited to screen capture software to analyze the usage of external resources, we present a more convenient way to capture this data, by combining the functionalities of CASMACAT with those of Inputlog, two state-of-the-art logging tools. We used this data to compare the types of resources used and the time spent in external resources for 40 from-scratch translation sessions (HT) and 40 post-editing (PE) sessions of 10 master’s students of translation (from English into Dutch). We took a closer look at the effect of the usage of external resources on productivity and quality of the final product. The types of resources consulted were comparable for HT and PE, but more time was spent in external resources when translating. Though search strategies seemed to be more successful when translating than when post-editing, the quality of the final product was comparable, and post-editing was faster than regular translation.

Identifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort

Translation Environment Tools make translators’ work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices’ translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected.