Neural retrieval models have superseded classic bag-of-words methods such as
BM25 as the retrieval framework of choice. However, neural systems lack the
interpretability of bag-of-words models; it is not trivial to connect a query
change to a change in the latent space that ultimately determines the retrieval
results. To shed light on this embedding space, we learn a "query decoder"
that, given a latent representation of a neural search engine, generates the
corresponding query. We show that it is possible to decode a meaningful query
from its latent representation and, when moving in the right direction in
latent space, to decode a query that retrieves the relevant paragraph. In
particular, the query decoder can be useful to understand "what should have
been asked" to retrieve a particular paragraph from the collection. We employ
the query decoder to generate a large synthetic dataset of query reformulations
for MSMarco, leading to improved retrieval performance. On this data, we train
a pseudo-relevance feedback (PRF) T5 model for the application of query
suggestion that outperforms both query reformulation and PRF information
retrieval baselines.

该研究通过学习一个 “查询解码器” 来理解神经搜索引擎的潜在嵌入空间，从而生成有意义的查询，生成大量合成查询并使用 PRF T5 模型进行查询建议，相比其他方法具有更好的检索性能。