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Understanding voluminous historical records provides clues on the past invarious aspects, such as social and political issues and even natural sciencefacts. However, it is generally difficult to fully utilize the historicalrecords, since most of the documents are not written in a modern language andpart of the contents are damaged over time. As a result, restoring the damagedor unrecognizable parts as well as translating the records into modernlanguages are crucial tasks. In response, we present a multi-task learningapproach to restore and translate historical documents based on aself-attention mechanism, specifically utilizing two Korean historical records,ones of the most voluminous historical records in the world. Experimentalresults show that our approach significantly improves the accuracy of thetranslation task than baselines without multi-task learning. In addition, wepresent an in-depth exploratory analysis on our translated results via topicmodeling, uncovering several significant historical events.

Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation