论文标题
使用软证据提取的自我训练方法用于机器阅读理解
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction
论文作者
论文摘要
神经模型在机器阅读理解（MRC）方面取得了巨大成功，其中许多通常由两个组成部分组成：一个证据提取器和一个答案预测指标。前者从参考文本中寻求最相关的信息，而后者是从提取的证据中找到或产生答案。尽管有证据标签在培训证据提取器方面很重要，但它们并不便宜，尤其是在许多非提取性MRC任务中，例如是/否问问题答案和多选择的MRC。 为了解决这个问题，我们提出了一种自我训练方法（STM），该方法在迭代过程中用自动生成的证据标签监督了证据提取器。在每次迭代中，基本MRC模型都经过金色答案和嘈杂的证据标签培训。训练有素的模型将在下一次迭代中预测伪证据标签是额外的监督。我们在三个MRC任务上评估了七个数据集的STM。实验结果证明了现有MRC模型的改善，我们还分析了这种自我训练方法在MRC中的作用以及为什么作用。可以从https://github.com/sparkjiao/self-training-mrc获得源代码
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 https://github.com/SparkJiao/Self-Training-MRC
