Neural models have achieved great success on machine reading comprehension
(MRC), many of which typically consist of two components: an evidence extractor
and an answer predictor. The former seeks the most relevant information from a
reference text, while the latter is to locate or generate answers from the
extracted evidence. Despite the importance of evidence labels for training the
evidence extractor, they are not cheaply accessible, particularly in many
non-extractive MRC tasks such as YES/NO question answering and multi-choice
MRC.
To address this problem, we present a Self-Training method (STM), which
supervises the evidence extractor with auto-generated evidence labels in an
iterative process. At each iteration, a base MRC model is trained with golden
answers and noisy evidence labels. The trained model will predict pseudo
evidence labels as extra supervision in the next iteration. We evaluate STM on
seven datasets over three MRC tasks. Experimental results demonstrate the
improvement on existing MRC models, and we also analyze how and why such a
self-training method works in MRC. The source code can be obtained from
this https URL

本研究提出了一种名为自学习方法的新技术，通过自动生成的证据标签迭代地监督证据提取器，从而提高了现有机器阅读理解模型的性能