Recent success of deep learning models for the task of extractive Question
Answering (QA) is hinged on the availability of large annotated corpora.
However, large domain specific annotated corpora are limited and expensive to
construct. In this work, we envision a system where the end user specifies a
set of base documents and only a few labelled examples. Our system exploits the
document structure to create cloze-style questions from these base documents;
pre-trains a powerful neural network on the cloze style questions; and further
fine-tunes the model on the labeled examples. We evaluate our proposed system
across three diverse datasets from different domains, and find it to be highly
effective with very little labeled data. We attain more than 50% F1 score on
SQuAD and TriviaQA with less than a thousand labelled examples. We are also
releasing a set of 3.2M cloze-style questions for practitioners to use while
building QA systems.

本研究提出一种利用基础文档和少量标注数据进行深度学习提取式问答的方法，并通过对三个不同领域数据集的实验验证了其有效性。

简单有效的半监督问答

Simple and Effective Semi-Supervised Question Answering

In this paper we study the problem of answering cloze-style questions over
documents. Our model, the Gated-Attention (GA) Reader, integrates a multi-hop
architecture with a novel attention mechanism, which is based on multiplicative
interactions between the query embedding and the intermediate states of a
recurrent neural network document reader. This enables the reader to build
query-specific representations of tokens in the document for accurate answer
selection. The GA Reader obtains state-of-the-art results on three benchmarks
for this task--the CNN \& Daily Mail news stories and the Who Did What dataset.
The effectiveness of multiplicative interaction is demonstrated by an ablation
study, and by comparing to alternative compositional operators for implementing
the gated-attention. The code is available at
this https URL

本文研究了如何在文档中回答填空题的问题，通过引入多跳结构和基于参考嵌入和递归神经网络的新型注意力机制，我们的 Gated-Attention Reader 模型能够生成查询特定的文档表示来准确地选择答案，并在三个基准数据集上获得最新的结果。