QuoteR：一个写作引用推荐的基准
QuoteR: A Benchmark of Quote Recommendation for Writing
摘要：
为了使我们的文章更优雅或更有说服力，使用引语是很常见的。为了帮助人们有效地找到合适的引文，提出了引文推荐任务，旨在推荐适合当前写作语境的引文。有各种报价推荐方法，但它们是在不同的未发表数据集上进行评估的。为了便于本课题的研究，我们构建了一个大型的、完全开放的引文推荐数据集QuoteR，该数据集包括英文、普通话和文言文三部分。它的任何部分都比以前未出版的对应部分大。我们对QuoteR上现有的报价推荐方法进行了广泛的评估。此外，我们提出了一个新的报价推荐模型，显着优于以前的方法QuoteR的所有三个部分。本文的所有代码和数据都可以在https://github.com/thunlp/QuoteR上获得。
英文摘要：
It is very common to use quotations (quotes) to make our writings more elegant or convincing. To help people find appropriate quotes efficiently, the task of quote recommendation is presented, aiming to recommend quotes that fit the current context of writing. There have been various quote recommendation approaches, but they are evaluated on different unpublished datasets. To facilitate the research on this task, we build a large and fully open quote recommendation dataset called QuoteR, which comprises three parts including English, standard Chinese and classical Chinese. Any part of it is larger than previous unpublished counterparts. We conduct an extensive evaluation of existing quote recommendation methods on QuoteR. Furthermore, we propose a new quote recommendation model that significantly outperforms previous methods on all three parts of QuoteR. All the code and data of this paper are available at https://github.com/thunlp/QuoteR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
