- Takaaki Matsumoto, T. Mitamura. 2017. MTMT in QALab-3: World History Essay Question Answering System that Utilizes Textbooks and Open Knowledge Bases. Abstract: This paper introduces the system and its evaluation for answering world history essay questions by utilizing linked open data which assists machine translation. Since the target questions are the world history subject of the entrance examination of the University of Tokyo, most answers can be found in the Japanese world history textbooks. However, an equivalent content of high-quality English translation of the Japanese world history textbooks is not available. Therefore, we try to translate those textbooks utilizing linked open data, and using source language knowledge resource of which content is not equivalent with the target knowledge resource. The evaluation result indicates that the proposed system shows the best ROUGE-1 scores of all the end-to-end submissions [13]. The result of this paper concludes followings. 1) Simple neural translation of knowledge resource does not work for domain-specific cross-lingual question answering. 2) Linked open data is effective to find correct translation for difficult terms in machine translation process. 3) Adding source language open knowledge resource would help even if its content is not equivalent to the target knowledge resources.
