Despite recent interest in open domain question answering (ODQA) over tables,
many studies still rely on datasets that are not truly optimal for the task
with respect to utilizing structural nature of table. These datasets assume
answers reside as a single cell value and do not necessitate exploring over
multiple cells such as aggregation, comparison, and sorting. Thus, we release
Open-WikiTable, the first ODQA dataset that requires complex reasoning over
tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be
applicable in the open-domain setting. As each question is coupled with both
textual answers and SQL queries, Open-WikiTable opens up a wide range of
possibilities for future research, as both reader and parser methods can be
applied. The dataset and code are publicly available.

本研究释出了 Open-WikiTable 数据集，开创了第一个需要对表格进行复杂推理的开放域问题回答（ODQA）数据集，旨在通过对 WikiSQL 和 WikiTableQuestions 的集成，更好地利用表格的结构性质。

Open-WikiTable: 一个用于开放域复杂推理表格问答的数据集

Open-WikiTable: Dataset for Open Domain Question Answering with Complex  Reasoning over Table

Though end-to-end neural approaches have recently been dominating NLP tasks
in both performance and ease-of-use, they lack interpretability and robustness.
We propose Binder, a training-free neural-symbolic framework that maps the task
input to a program, which (1) allows binding a unified API of language model
(LM) functionalities to a programming language (e.g., SQL, Python) to extend
its grammar coverage and thus tackle more diverse questions, (2) adopts an LM
as both the program parser and the underlying model called by the API during
execution, and (3) requires only a few in-context exemplar annotations.
Specifically, we employ GPT-3 Codex as the LM. In the parsing stage, with only
a few in-context exemplars, Codex is able to identify the part of the task
input that cannot be answerable by the original programming language, correctly
generate API calls to prompt Codex to solve the unanswerable part, and identify
where to place the API calls while being compatible with the original grammar.
In the execution stage, Codex can perform versatile functionalities (e.g.,
commonsense QA, information extraction) given proper prompts in the API calls.
Binder achieves state-of-the-art results on WikiTableQuestions and TabFact
datasets, with explicit output programs that benefit human debugging. Note that
previous best systems are all finetuned on tens of thousands of task-specific
samples, while Binder only uses dozens of annotations as in-context exemplars
without any training. Our code is available at this https URL .

提出 Binder，一种基于神经符号方法的框架，采用 GPT-3 Codex 作为输入的语言模型，通过少量标注建立程序，实现对丰富的问题进行回答，取得了在 WikiTableQuestions 和 TabFact 两个数据集上的最先进结果。