使用图像标题和多任务学习推荐查询重构
Using Image Captions and Multitask Learning for Recommending Query Reformulations
摘要：
交互式搜索会话通常包含多个查询，其中用户提交先前查询的重构版本以响应原始结果。我们的目标是提高商业图像搜索引擎的查询推荐体验。我们提出的方法结合了当前最先进的做法，从相关文献-使用基于生成的序列到序列模型，捕获会话上下文，和多任务架构，同时优化结果的排名。我们扩展了这种设置，通过使用点击图像的标题作为目标来驱动这种模型的学习，而不是使用会话中的后续查询。由于这些标题往往在语言上更丰富，因此可以将重构机制视为有助于构建更具描述性的查询。此外，通过使用成对损失的二级排名任务，我们表明，所产生的重新更多样化。
英文摘要：
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.
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