Question answering (QA) is the task of answering questions posed in natural
language with free-form natural language answers extracted from a given
passage. In the OpenQA variant, only a question text is given, and the system
must retrieve relevant passages from an unstructured knowledge source and use
them to provide answers, which is the case in the mainstream QA systems on the
Web. QA systems currently are mostly limited to the English language due to the
lack of large-scale labeled QA datasets in non-English languages. In this
paper, we show that effective, low-cost OpenQA systems can be developed for
low-resource languages. The key ingredients are (1) weak supervision using
machine-translated labeled datasets and (2) a relevant unstructured knowledge
source in the target language. Furthermore, we show that only a few hundred
gold assessment examples are needed to reliably evaluate these systems. We
apply our method to Turkish as a challenging case study, since English and
Turkish are typologically very distinct. We present SQuAD-TR, a machine
translation of SQuAD2.0, and we build our OpenQA system by adapting ColBERT-QA
for Turkish. We obtain a performance improvement of 9-34% in the EM score and
13-33% in the F1 score compared to the BM25-based and DPR-based baseline QA
reader models by using two versions of Wikipedia dumps spanning two years. Our
results show that SQuAD-TR makes OpenQA feasible for Turkish, which we hope
encourages researchers to build OpenQA systems in other low-resource languages.
We make all the code, models, and the dataset publicly available.

在这篇论文中，我们展示了能够为低资源语言开发有效且成本低廉的 OpenQA 系统的关键要素，其中包括利用机器翻译标注数据的弱监督和目标语言中相关的非结构化知识源。我们以土耳其语为挑战性案例研究，通过 ColBERT-QA 对 SQuAD-TR 进行了调整来构建我们的 OpenQA 系统。在使用两个跨足两年的维基百科转储版本的基础上，我们与基于 BM25 和 DPR 的 QA 读取器模型相比，在 EM 得分上获得了 9-34% 的性能提升以及 F1 得分上的 13-33% 的性能提升。我们希望我们的结果可以鼓励研究人员在其他低资源语言中构建 OpenQA 系统，并将所有的代码、模型和数据集公开提供。

为低资源语言构建高效高效的 OpenQA 系统

Building Efficient and Effective OpenQA Systems for Low-Resource  Languages

Open domain question answering (OpenQA) tasks have been recently attracting
more and more attention from the natural language processing (NLP) community.
In this work, we present the first free-form multiple-choice OpenQA dataset for
solving medical problems, MedQA, collected from the professional medical board
exams. It covers three languages: English, simplified Chinese, and traditional
Chinese, and contains 12,723, 34,251, and 14,123 questions for the three
languages, respectively. We implement both rule-based and popular neural
methods by sequentially combining a document retriever and a machine
comprehension model. Through experiments, we find that even the current best
method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the
English, traditional Chinese, and simplified Chinese questions, respectively.
We expect MedQA to present great challenges to existing OpenQA systems and hope
that it can serve as a platform to promote much stronger OpenQA models from the
NLP community in the future.

本研究提供了首个针对医疗问题的自由形式多项选择 OpenQA 数据集 MedQA，涵盖三种语言，并通过实现规则和神经网络两种方法进行实验，最终发现即使是当前最好的方法在英语、繁体中文和简体中文问题上也只能实现 36.7％、42.0％和 70.1％的测试准确率，希望这个数据集能够为推动未来更强大的 NLP OpenQA 模型的发展提供平台。