  We propose a simple yet effective approach for improving Korean word
representations using additional linguistic annotation (i.e. Hanja). We employ
cross-lingual transfer learning in training word representations by leveraging
the fact that Hanja is closely related to Chinese. We evaluate the intrinsic
quality of representations learned through our approach using the word analogy
and similarity tests. In addition, we demonstrate their effectiveness on
several downstream tasks, including a novel Korean news headline generation
task.
