论文标题
临时信息检索的神经文档扩展
Neural document expansion for ad-hoc information retrieval
论文作者
论文摘要
最近，Nogueira等。 [2019]提出了一种基于神经SEQ2SEQ模型的新方法来记录扩展的方法，显示出短文检索任务的显着改善。但是，这种方法需要大量的内域培训数据。在本文中，我们表明，这种神经文档扩展方法可以有效地适应标准的IR任务，在该任务中，标签稀缺并且存在许多长文档。
Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task. However, this approach needs a large amount of in-domain training data. In this paper, we show that this neural document expansion approach can be effectively adapted to standard IR tasks, where labels are scarce and many long documents are present.
