In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers: we inject the relevance score of the lexical model as a token in the middle of the input of the cross-encoder re-ranker. It was shown in prior work that interpolation between the relevance score of lexical and BERT-based re-rankers may not consistently result in higher effectiveness. Our idea is motivated by the finding that BERT models can capture numeric information. We compare several representations of the BM25 score and inject them as text in the input of four different cross-encoders. We additionally analyze the effect for different query types, and investigate the effectiveness of our method for capturing exact matching relevance. Evaluation on the MSMARCO Passage collection and the TREC DL collections shows that the proposed method significantly improves over all cross-encoder re-rankers as well as the common interpolation methods. We show that the improvement is consistent for all query types. We also find an improvement in exact matching capabilities over both BM25 and the cross-encoders. Our findings indicate that cross-encoder re-rankers can efficiently be improved without additional computational burden and extra steps in the pipeline by explicitly adding the output of the first-stage ranker to the model input, and this effect is robust for different models and query types.
翻译:在本文中,我们提出了一个将第一阶段的词汇检索模型和基于变换器的重新排序器合并在一起的新办法:我们将词汇模型的相关性评分作为跨编码器重新排序器输入中间的一个象征;我们以前的工作表明,基于词汇的重新排序器的相关性评分与基于BERT的重新排序器的相关性评分之间的内插可能不会始终产生更高的效果。我们的想法的动力在于发现BERT模型能够捕捉数字信息。我们比较了BB25评分的若干表示,并将其作为四个不同跨编码器输入的文本。我们进一步分析了不同查询类型的效果,并调查了我们获取精确匹配相关性的方法的有效性。对MSMARCO过关器采集和TREC DL采集的评估表明,拟议的方法大大改进了所有跨编码重新排序器重新排序器以及通用的内插模型。我们发现,所有查询类型的改进是一致的。我们还发现,在BB25和跨编码器的输入中,跨编码器和跨编码器的转换器的精确性能力得到了改进。我们的调查结果表明,通过不增加阶梯的阶梯的升级后,可以使跨阶梯的再升级的计算结果得到改进。