论文标题
可解释且预算约束的上下文化以重新排序
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking
论文作者
论文摘要
搜索引擎在严格的时间限制下运行，因为快速响应对于用户满意度至关重要。因此，神经重新排行模型的时间预算有限，可以重新排名文档。在相同的时间内，更快的重新排行模型可以包含更多的文档，而不是效率较低的文档，从而提高了效率更高。为了利用此属性，我们提出了TK（变压器 - 内核）：使用有效的上下文化机制进行临时搜索的神经重新排列模型。 TK使用少量的变压器层（最多三个）来将查询和文档单词嵌入方式进行上下文化。为了评分单个术语互动，我们使用文档长度增强的内核模拟，使用户能够深入了解该模型。 TK提供了有效性和效率之间的最佳比率：在现实的时间限制下（每个查询最多200 ms），与BERT和其他重新排列模型相比，TK实现了最高的有效性。我们在三个大规模排名集合中进行了证明：MSMARCO-PASSAGE，MSMARCO文档和TREC汽车。此外，为了深入了解TK，我们对TK的结果进行了群集查询分析，突出了其对具有不同信息需求的查询的优势和缺点，我们展示了如何通过比较其内部分数来解释两个文档的排名差异的原因。
Search engines operate under a strict time constraint as a fast response is paramount to user satisfaction. Thus, neural re-ranking models have a limited time-budget to re-rank documents. Given the same amount of time, a faster re-ranking model can incorporate more documents than a less efficient one, leading to a higher effectiveness. To utilize this property, we propose TK (Transformer-Kernel): a neural re-ranking model for ad-hoc search using an efficient contextualization mechanism. TK employs a very small number of Transformer layers (up to three) to contextualize query and document word embeddings. To score individual term interactions, we use a document-length enhanced kernel-pooling, which enables users to gain insight into the model. TK offers an optimal ratio between effectiveness and efficiency: under realistic time constraints (max. 200 ms per query) TK achieves the highest effectiveness in comparison to BERT and other re-ranking models. We demonstrate this on three large-scale ranking collections: MSMARCO-Passage, MSMARCO-Document, and TREC CAR. In addition, to gain insight into TK, we perform a clustered query analysis of TK's results, highlighting its strengths and weaknesses on queries with different types of information need and we show how to interpret the cause of ranking differences of two documents by comparing their internal scores.
