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MT Summit, Nagoya, Japan, September 2017
Harvesting Polysemous Terms from e-commerce Data to Enhance QA
Polysemous words can be difficult to translate and can affect the quality of Machine Translation (MT) output. Once the MT quality is affected, it has a direct impact on post-editing and on human-assisted machine translation. The presence of these terms increases the risk of errors. We think that these important words can be used to improve and to measure quality of translations. We present three methods for finding these words from e-commerce data, based on Named Entity Recognition, Part-of-Speech and Search Queries.
The use of post-editing of machine translation output is increasing throughout the language technology community. In this work, we investigate whether the MT system influences the human translator, thereby introducing "bias" and potentially leading to errors in the post-editing. We analyze how often a translator accepts an incorrect suggestion from the MT system and determine different types of bias errors. We carry out quantitative analysis on translations of eCommerce data from English into Portuguese, consisting of 713 segments with about 15k words. We observed a higher-than-expected number of bias errors, about 18 bias errors per 1,000 words. Among the most frequent types of bias error we observed ambiguous modifiers, terminology errors, polysemy, and omissions. The goal of this work is to provide quantitative data about bias errors in post-editing that help indicate the existence of bias. We explore some ideas on how to automate the finding of these error patterns and facilitate the quality assurance of post-editing.