Existing question answering systems mainly focus on dealing with text data.
However, much of the data produced daily is stored in the form of tables that
can be found in documents and relational databases, or on the web. To solve the
task of question answering over tables, there exist many datasets for table
question answering written in English, but few Korean datasets. In this paper,
we demonstrate how we construct Korean-specific datasets for table question
answering: Korean tabular dataset is a collection of 1.4M tables with
corresponding descriptions for unsupervised pre-training language models.
Korean table question answering corpus consists of 70k pairs of questions and
answers created by crowd-sourced workers. Subsequently, we then build a
pre-trained language model based on Transformer and fine-tune the model for
table question answering with these datasets. We then report the evaluation
results of our model. We make our datasets publicly available via our GitHub
repository and hope that those datasets will help further studies for question
answering over tables, and for the transformation of table formats.

本文构建了韩国特定的数据集，包括 1.4M 个表和 70k 个问题。建立了基于 Transformer 的预训练语言模型以及针对这些数据集进行的表问答模型的微调。

面向韩国的表格问答数据集

Korean-Specific Dataset for Table Question Answering

Recent advances in open-domain QA have led to strong models based on dense
retrieval, but only focused on retrieving textual passages. In this work, we
tackle open-domain QA over tables for the first time, and show that retrieval
can be improved by a retriever designed to handle tabular context. We present
an effective pre-training procedure for our retriever and improve retrieval
quality with mined hard negatives. As relevant datasets are missing, we extract
a subset of Natural Questions (Kwiatkowski et al., 2019) into a Table QA
dataset. We find that our retriever improves retrieval results from 72.0 to
81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a
BERT based retriever.

本研究针对开放领域问题回答系统的表格数据提出了一种有效的检索方法，并通过训练和挖掘难例等技术，提高了回答的准确性和表现。

基于密集检索的表格开放领域问答

Open Domain Question Answering over Tables via Dense Retrieval

The scientific literature is growing exponentially, and professionals are no
more able to cope with the current amount of publications. Text mining provided
in the past methods to retrieve and extract information from text; however,
most of these approaches ignored tables and figures. The research done in
mining table data still does not have an integrated approach for mining that
would consider all complexities and challenges of a table. Our research is
examining the methods for extracting numerical (number of patients, age, gender
distribution) and textual (adverse reactions) information from tables in the
clinical literature. We present a requirement analysis template and an integral
methodology for information extraction from tables in clinical domain that
contains 7 steps: (1) table detection, (2) functional processing, (3)
structural processing, (4) semantic tagging, (5) pragmatic processing, (6) cell
selection and (7) syntactic processing and extraction. Our approach performed
with the F-measure ranged between 82 and 92%, depending on the variable, task
and its complexity.

该研究提出了一种从医学文献中的表格中提取数值和文本信息的方法，包括表格检测、功能处理、结构处理、语义标记、语用处理、单元选择和句法处理等七个步骤，F - 度量值范围在 82% - 92% 之间.