To help individuals express themselves better, quotation recommendation is receiving growing attention. Nevertheless, most prior efforts focus on modeling quotations and queries separately and ignore the relationship between the quotations and the queries. In this work, we introduce a transformation matrix that directly maps the query representations to quotation representations. To better learn the mapping relationship, we employ a mapping loss that minimizes the distance of two semantic spaces (one for quotation and another for mapped-query). Furthermore, we explore using the words in history queries to interpret the figurative language of quotations, where quotation-aware attention is applied on top of history queries to highlight the indicator words. Experiments on two datasets in English and Chinese show that our model outperforms previous state-of-the-art models.

该研究介绍了一个直接将查询表示映射到引言表示的转换矩阵，并采用一种映射损失来更好地学习映射关系；此外，还探索使用历史查询中的单词来解释引语的比喻语言，并且在历史查询的基础上应用引文感知注意机制来突出展示关键词。该模型在英文和中文的两个数据集上取得了优于之前的最先进模型的表现。

基于查询到引语的转换的引语推荐与解释