论文标题
以实体为中心的查询细化
Entity-Centric Query Refinement
论文作者
论文摘要
我们介绍了以实体为中心的查询改进的任务。给定一个输入查询，其答案是（潜在的）实体集合，任务输出是一小部分查询精炼，旨在帮助用户进行有效的域探索和实体发现。我们提出了一种为此任务创建培训数据集的方法。对于给定的输入查询，我们使用现有的知识基础分类法作为候选查询细化的来源，并使用旨在将回答输入查询的实体集的搜索过程中的搜索过程中的这些候选者选择中的最终组成。我们证明我们的方法确定了人类注释者认为有趣，全面和不冗余的精炼集。此外，我们发现，在新结构的数据集中训练的文本生成模型能够为现有分类法所没有涵盖的新型查询提供改进。我们的代码和数据可在https://github.com/google-research/language/tree/master/master/language/qresp上找到。
We introduce the task of entity-centric query refinement. Given an input query whose answer is a (potentially large) collection of entities, the task output is a small set of query refinements meant to assist the user in efficient domain exploration and entity discovery. We propose a method to create a training dataset for this task. For a given input query, we use an existing knowledge base taxonomy as a source of candidate query refinements, and choose a final set of refinements from among these candidates using a search procedure designed to partition the set of entities answering the input query. We demonstrate that our approach identifies refinement sets which human annotators judge to be interesting, comprehensive, and non-redundant. In addition, we find that a text generation model trained on our newly-constructed dataset is able to offer refinements for novel queries not covered by an existing taxonomy. Our code and data are available at https://github.com/google-research/language/tree/master/language/qresp.
