论文标题
使用图像标题和多任务学习来推荐查询重新进行
Using Image Captions and Multitask Learning for Recommending Query Reformulations
论文作者
论文摘要
交互式搜索会话通常包含多个查询，其中用户根据原始结果提交了上一个查询的重新印刷版本。我们旨在增强商业图像搜索引擎的查询建议体验。我们提出的方法结合了相关文献中当前的最新实践 - 使用基于生成的序列到序列模型来捕获会话上下文，以及同时优化结果排名的多任务体系结构。我们通过学习以单击图像为目标的字幕的学习模型来扩展此设置，而不是使用会话中的后续查询。由于这些标题在语言上往往更富裕，因此可以将重新制定机制视为构建更多描述性查询的帮助。此外，通过将成对损失用于二级排名任务，我们表明生成的重新恢复更加多样化。
Interactive search sessions often contain multiple queries, where the user submits a reformulated version of the previous query in response to the original results. We aim to enhance the query recommendation experience for a commercial image search engine. Our proposed methodology incorporates current state-of-the-art practices from relevant literature -- the use of generation-based sequence-to-sequence models that capture session context, and a multitask architecture that simultaneously optimizes the ranking of results. We extend this setup by driving the learning of such a model with captions of clicked images as the target, instead of using the subsequent query within the session. Since these captions tend to be linguistically richer, the reformulation mechanism can be seen as assistance to construct more descriptive queries. In addition, via the use of a pairwise loss for the secondary ranking task, we show that the generated reformulations are more diverse.
