Knowledge and common knowledge in a distributed environment
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ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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In this paper, we present a protocol for collaborative translation, where two non-bilingual people who use different languages collaborate to perform the task of translation using machine translation (MT) services. Members in one real life example of intercultural collaboration try to share information more effectively by modifying unnatural machine translated sentences manually and improving their fluency. However, there are two problems with this method: One is that poor quality of translation can induce misinterpretations, and the other is that phrases in the machine translated sentence that a person cannot make sense of remain unmodified. The proposed protocol is designed to solve these problems. More concretely, one person, who handles the source language and knows the original sentence (source language side), evaluates the adequacy between the original sentence and the translation of the sentence modified to be fluent by the other person, who handles the target language (target language side). In addition, by determining whether the meaning of the machine translated sentence is understandable, it is ensured that the two non-bilingual people do above tasks properly. As a result, this protocol 1) improves MT quality; and 2) terminates successfully only when the translation result becomes adequate and fluent. The experiment results show that when the protocol terminates successfully, the quality of the translation increases to about 83 percent in Japanese-English translation and 91 percent in Japanese-Chinese translation.