Commonsense question answering (CQA) aims to test if models can answer
questions regarding commonsense knowledge that everyone knows. Prior works that
incorporate external knowledge bases have shown promising results, but
knowledge bases are expensive to construct and are often limited to a fixed set
of relations. In this paper, we instead focus on better utilizing the
\textit{implicit knowledge} stored in pre-trained language models. While
researchers have found that the knowledge embedded in pre-trained language
models can be extracted by having them fill in the blanks of carefully designed
prompts for relation extraction and text classification, it remains unclear if
we can adopt this paradigm in CQA where the inputs and outputs take much more
flexible forms. To this end, we investigate four translation methods that can
translate natural questions into cloze-style sentences to better solicit
commonsense knowledge from language models, including a syntactic-based model,
an unsupervised neural model, and two supervised neural models. In addition, to
combine the different translation methods, we propose to encourage consistency
among model predictions on different translated questions with unlabeled data.
We demonstrate the effectiveness of our methods on three CQA datasets in
zero-shot settings. We show that our methods are complementary to a knowledge
base improved model, and combining them can lead to state-of-the-art zero-shot
performance. Analyses also reveal distinct characteristics of the different
cloze translation methods and provide insights on why combining them can lead
to great improvements.

本研究针对常识问题回答（Commonsense question answering）的挑战，提出了利用预训练语言模型中的 “隐含知识”，通过四种翻译方法，探索如何更好地调动语言模型中的常识知识。研究通过在零样本情况下对三个 CQA 数据集进行实验，证明了该方法对增强模型的性能十分有效，并且结合外部知识库进一步提高了模型性能。同时，文章还分析了各种填空翻译方法的不同特点和优缺点。

用填空翻译和一致性优化实现零样本通识常识问答

Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization

In multilingual question answering, either the question needs to be
translated into the document language, or vice versa. In addition to direction,
there are multiple methods to perform the translation, four of which we explore
in this paper: word-based, 10-best, context-based, and grammar-based. We build
a feature for each combination of translation direction and method, and train a
model that learns optimal feature weights. On a large forum dataset consisting
of posts in English, Arabic, and Chinese, our novel learn-to-translate approach
was more effective than a strong baseline (p<0.05): translating all text into
English, then training a classifier based only on English (original or
translated) text.

该研究探讨多语种问答中的翻译方法，并提出基于学习的翻译方法，通过模型训练并结合语言分类器实现翻译，结果比强基准模型表现更优。