Tables are an important form of structured data for both human and machine
readers alike, providing answers to questions that cannot, or cannot easily, be
found in texts. Recent work has designed special models and training paradigms
for table-related tasks such as table-based question answering and table
retrieval. Though effective, they add complexity in both modeling and data
acquisition compared to generic text solutions and obscure which elements are
truly beneficial. In this work, we focus on the task of table retrieval, and
ask: "is table-specific model design necessary for table retrieval, or can a
simpler text-based model be effectively used to achieve a similar result?"
First, we perform an analysis on a table-based portion of the Natural Questions
dataset (NQ-table), and find that structure plays a negligible role in more
than 70% of the cases. Based on this, we experiment with a general Dense
Passage Retriever (DPR) based on text and a specialized Dense Table Retriever
(DTR) that uses table-specific model designs. We find that DPR performs well
without any table-specific design and training, and even achieves superior
results compared to DTR when fine-tuned on properly linearized tables. We then
experiment with three modules to explicitly encode table structures, namely
auxiliary row/column embeddings, hard attention masks, and soft relation-based
attention biases. However, none of these yielded significant improvements,
suggesting that table-specific model design may not be necessary for table
retrieval.

本研究旨在探究是否需要使用特别设计的表格型模型进行表格信息检索，结果表明在大部分情况下使用基于文本的模型，如 Dense Passage Retriever (DPR) 即可达到相似或更优秀的结果，无需特别设计。通过实验，辅助行 / 列嵌入、硬性注意力蒙版和软关系注意力偏差等三个模块也未能产生显著提高效果，表明使用特别设计的模型并非必要。

表格检索可能不需要特定于表格的模型设计

Table Retrieval May Not Necessitate Table-specific Model Design

Neural passage retrieval is a new and promising approach in open retrieval
question answering. In this work, we stress-test the Dense Passage Retriever
(DPR) -- a state-of-the-art (SOTA) open domain neural retrieval model -- on
closed and specialized target domains such as COVID-19, and find that it lags
behind standard BM25 in this important real-world setting. To make DPR more
robust under domain shift, we explore its fine-tuning with synthetic training
examples, which we generate from unlabeled target domain text using a
text-to-text generator. In our experiments, this noisy but fully automated
target domain supervision gives DPR a sizable advantage over BM25 in
out-of-domain settings, making it a more viable model in practice. Finally, an
ensemble of BM25 and our improved DPR model yields the best results, further
pushing the SOTA for open retrieval QA on multiple out-of-domain test sets.

本研究针对新的神经通道检索方法加以探讨，发现在 COVID-19 的封闭和专业目标域中，SOTA 模型 Dense Passage Retriever 比标准 BM25 差距较大。通过用文本生成器来生成合成训练示例对其进行微调，使其在对外域数据处理中更具鲁棒性，效果有所提高。最终，BM25 与改进的 DPR 模型的集成产生了最佳结果，进一步推动了开放式检索 QA 的 SOTA，适用于多个出域测试集。