BERT-based information retrieval models are expensive, in both time (query
latency) and computational resources (energy, hardware cost), making many of
these models impractical especially under resource constraints. The reliance on
a query encoder that only performs tokenization and on the pre-processing of
passage representations at indexing, has allowed the recently proposed TILDE
method to overcome the high query latency issue typical of BERT-based models.
This however is at the expense of a lower effectiveness compared to other
BERT-based re-rankers and dense retrievers. In addition, the original TILDE
method is characterised by indexes with a very high memory footprint, as it
expands each passage into the size of the BERT vocabulary. In this paper, we
propose TILDEv2, a new model that stems from the original TILDE but that
addresses its limitations. TILDEv2 relies on contextualized exact term matching
with expanded passages. This requires to only store in the index the score of
tokens that appear in the expanded passages (rather than all the vocabulary),
thus producing indexes that are 99% smaller than those of TILDE. This matching
mechanism also improves ranking effectiveness by 24%, without adding to the
query latency. This makes TILDEv2 the state-of-the-art passage re-ranking
method for CPU-only environments, capable of maintaining query latency below
100ms on commodity hardware.

提出了一种名为 TILDEv2 的新模型，该模型基于原始的 TILDE，但解决了其局限性，采用扩展的句子进行上下文化精确术语匹配，并将所得分数存储在索引中，生成的索引不到 TILDE 索引的 1％，且提高排名效果 24％，不影响查询延迟。它是基于 CPU 的环境下维持低于 100ms 的查询延迟的最先进的语段重新排序方法。