Successfully training a deep neural network demands a huge corpus of labeled data. However, each label only provides limited information to learn from and collecting the requisite number of labels involves massive human effort. In this work, we introduce LEAN-LIFE, a web-based, Label-Efficient AnnotatioN framework for sequence labeling and classification tasks, with an easy-to-use UI that not only allows an annotator to provide the needed labels for a task, but also enables LearnIng From Explanations for each labeling decision. Such explanations enable us to generate useful additional labeled data from unlabeled instances, bolstering the pool of available training data. On three popular NLP tasks (named entity recognition, relation extraction, sentiment analysis), we find that using this enhanced supervision allows our models to surpass competitive baseline F1 scores by more than 5-10 percentage points, while using 2X times fewer labeled instances. Our framework is the first to utilize this enhanced supervision technique and does so for three important tasks -- thus providing improved annotation recommendations to users and an ability to build datasets of (data, label, explanation) triples instead of the regular (data, label) pair.

本文介绍了 LEAN-LIFE，一种基于 web 的标注框架，它具有易于使用的 UI，可以为序列标记和分类任务提供所需的标签和决策解释，并在三个流行的 NLP 任务上显示，使用这种增强型监督方法可减少标注实例数量，并且提高了超过 5-10 个百分点的基线 F1 分数。

LEAN-LIFE: 一个标签效率的注解框架，用于从解释中学习