Pretrained contextualized language models such as BERT have achieved
impressive results on various natural language processing benchmarks.
Benefiting from multiple pretraining tasks and large scale training corpora,
pretrained models can capture complex syntactic word relations. In this paper,
we use the deep contextualized language model BERT for the task of ad hoc table
retrieval. We investigate how to encode table content considering the table
structure and input length limit of BERT. We also propose an approach that
incorporates features from prior literature on table retrieval and jointly
trains them with BERT. In experiments on public datasets, we show that our best
approach can outperform the previous state-of-the-art method and BERT baselines
with a large margin under different evaluation metrics.

本文使用预训练的 BERT 模型来处理表格检索任务，研究如何编码表格内容以及如何将其他文献中的特征与 BERT 模型联合训练，实验表明这一方法在公共数据集上表现卓越。

使用深度上下文语言模型进行表格搜索

Table Search Using a Deep Contextualized Language Model

We introduce and address the problem of ad hoc table retrieval: answering a
keyword query with a ranked list of tables. This task is not only interesting
on its own account, but is also being used as a core component in many other
table-based information access scenarios, such as table completion or table
mining. The main novel contribution of this work is a method for performing
semantic matching between queries and tables. Specifically, we (i) represent
queries and tables in multiple semantic spaces (both discrete sparse and
continuous dense vector representations) and (ii) introduce various similarity
measures for matching those semantic representations. We consider all possible
combinations of semantic representations and similarity measures and use these
as features in a supervised learning model. Using a purpose-built test
collection based on Wikipedia tables, we demonstrate significant and
substantial improvements over a state-of-the-art baseline.

地址 “临时表检索” 的问题，提出了一种通过语义匹配查询和表格的方法，通过在多个语义空间中表示查询和表格，并采用各种相似性度量将这些特征用于监督式学习模型中，针对维基百科表格集合进行了实验，并取得了显著和实质性的改进。