Historical records in Korea before the 20th century were primarily written in
Hanja, an extinct language based on Chinese characters and not understood by
modern Korean or Chinese speakers. Historians with expertise in this time
period have been analyzing the documents, but that process is very difficult
and time-consuming, and language models would significantly speed up the
process. Toward building and evaluating language models for Hanja, we release
the Hanja Understanding Evaluation dataset consisting of chronological
attribution, topic classification, named entity recognition, and summary
retrieval tasks. We also present BERT-based models continued training on the
two major corpora from the 14th to the 19th centuries: the Annals of the Joseon
Dynasty and Diaries of the Royal Secretariats. We compare the models with
several baselines on all tasks and show there are significant improvements
gained by training on the two corpora. Additionally, we run zero-shot
experiments on the Daily Records of the Royal Court and Important Officials
(DRRI). The DRRI dataset has not been studied much by the historians, and not
at all by the NLP community.

本篇研究旨在建立、评估韩字理解的语言模型，为此发布了包含了年代归属、主题分类、命名实体识别和概要检索任务的 Hanja Understanding Evaluation 数据集。同时，还在朝鲜王朝的官方纪事 Annals 和 Royal Secretariats 的两个重要文献语料库上训练了 BERT 模型，并将它与几个基线模型进行了比较，发现训练在这两个文献上的模型具有显著改进。此外，还在历史学研究中尚未被广泛研究的 DRRI 数据集上进行了零样本实验。

HUE: 用于理解韩国古代汉字文献的预训练模型和数据集

HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea

The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of
Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals
were originally written in an archaic Korean writing system, `Hanja', and were
translated into Korean from 1968 to 1993. The resulting translation was however
too literal and contained many archaic Korean words; thus, a new expert
translation effort began in 2012. Since then, the records of only one king have
been completed in a decade. In parallel, expert translators are working on
English translation, also at a slow pace and produced only one king's records
in English so far. Thus, we propose H2KE, a neural machine translation model,
that translates historical documents in Hanja to more easily understandable
Korean and to English. Built on top of multilingual neural machine translation,
H2KE learns to translate a historical document written in Hanja, from both a
full dataset of outdated Korean translation and a small dataset of more
recently translated contemporary Korean and English. We compare our method
against two baselines: a recent model that simultaneously learns to restore and
translate Hanja historical document and a Transformer based model trained only
on newly translated corpora. The experiments reveal that our method
significantly outperforms the baselines in terms of BLEU scores for both
contemporary Korean and English translations. We further conduct extensive
human evaluation which shows that our translation is preferred over the
original expert translations by both experts and non-expert Korean speakers.

该研究提出一种基于神经机器翻译的模型 H2KE，用于将朝鲜王朝的史料由汉字文献翻译成易于理解的现代韩文和英文，并发现该方法在 BLEU 分数和人工评估中相对于传统方法有更好的表现。

将汉字历史文献翻译成现代韩语和英语

Translating Hanja Historical Documents to Contemporary Korean and English

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.

本文提出一种使用汉字等额外语言注释来改善韩文单词表示的简单而有效的方法，通过跨语言迁移学习来训练单词表示，以检验通过此方法学习到的表示的内在质量，并证明其在几种下游任务中的有效性，包括一项新颖的韩国新闻标题生成任务。