论文标题
弱和半监督的证据提取
Weakly- and Semi-supervised Evidence Extraction
论文作者
论文摘要
对于许多预测任务，利益相关者不仅希望预测，还希望支持人类可以用来验证其正确性的证据。但是，实际上，标记支持证据的其他注释只能用于少数培训示例（如果有的话）。在本文中，我们提出了新的方法，以结合少数证据注释（强有力的半义vision）与丰富的文件级标签（弱监督），以提取证据提取。在评估具有证据注释的两个分类任务时，我们发现我们的方法的表现优于从可解释性文献到我们的任务的基准。我们的方法获得了可观的收益，只有一百个证据注释。可以在https://github.com/danishpruthi/vidence-extraction上获得复制我们工作的代码和数据集。
For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness. However, in practice, additional annotations marking supporting evidence may only be available for a minority of training examples (if available at all). In this paper, we propose new methods to combine few evidence annotations (strong semi-supervision) with abundant document-level labels (weak supervision) for the task of evidence extraction. Evaluating on two classification tasks that feature evidence annotations, we find that our methods outperform baselines adapted from the interpretability literature to our task. Our approach yields substantial gains with as few as hundred evidence annotations. Code and datasets to reproduce our work are available at https://github.com/danishpruthi/evidence-extraction.
