Recent progress in information retrieval finds that embedding query and
document representation into multi-vector yields a robust bi-encoder retriever
on out-of-distribution datasets. In this paper, we explore whether late
interaction, the simplest form of multi-vector, is also helpful to neural
rerankers that only use the [CLS] vector to compute the similarity score.
Although intuitively, the attention mechanism of rerankers at the previous
layers already gathers the token-level information, we find adding late
interaction still brings an extra 5% improvement in average on
out-of-distribution datasets, with little increase in latency and no
degradation in in-domain effectiveness. Through extensive experiments and
analysis, we show that the finding is consistent across different model sizes
and first-stage retrievers of diverse natures and that the improvement is more
prominent on longer queries.

本文探讨了将后期交互用于神经重排器的有用性，以提高其在分布外数据集中的性能，该方法能带来额外的 5％改进，而无需增加太多的延迟和没有影响其在域内数据上的效果，通过实验和分析，发现这一结论与模型大小和不同类型的第一阶段检索器一致，并且这种改进在较长的查询上更为显著。

通过上下文化的后期交互提高神经重排器的超领域泛化能力

Improving Out-of-Distribution Generalization of Neural Rerankers with Contextualized Late Interaction

Incorporating external knowledge sources effectively in conversations is a
longstanding problem in open-domain dialogue research. The existing literature
on open-domain knowledge selection is limited and makes certain brittle
assumptions on knowledge sources to simplify the overall task (Dinan et al.,
2019), such as the existence of a single relevant knowledge sentence per
context. In this work, we evaluate the existing state of open-domain
conversation knowledge selection, showing where the existing methodologies
regarding data and evaluation are flawed. We then improve on them by proposing
a new framework for collecting relevant knowledge, and create an augmented
dataset based on the Wizard of Wikipedia (WOW) corpus, which we call WOW++.
WOW++ averages 8 relevant knowledge sentences per dialogue context, embracing
the inherent ambiguity of open-domain dialogue knowledge selection. We then
benchmark various knowledge ranking algorithms on this augmented dataset with
both intrinsic evaluation and extrinsic measures of response quality, showing
that neural rerankers that use WOW++ can outperform rankers trained on standard
datasets.

提出对话知识选择的新框架和数据集，用于评估现有方法的不足之处，并通过神经重排序模型改进评估结果，出色地完成了对话回复