Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations
This work addresses the need for better self-expression tools through quotation recommendation, offering an incremental improvement by modeling the relationship between queries and quotations more effectively.
The paper tackles the problem of quotation recommendation by introducing a transformation matrix to directly map query representations to quotation representations, achieving improved performance over previous state-of-the-art models on English and Chinese datasets.
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.