论文标题
有关通过人类解释改善NLP模型的调查
A survey on improving NLP models with human explanations
论文作者
论文摘要
培训可以访问人类解释的模型可以提高数据效率和模型性能在内外数据上。除了这些经验发现，与人类学习过程的相似之处使从解释中学习成为建立富有成果的人机相互作用的有希望的方法。已经提出了几种使用人类解释来改善自然语言处理（NLP）模型的方法，这些方法依赖于将这些解释整合到学习过程中的不同解释类型和机制。这些方法很少彼此比较，因此从业者很难为特定用例选择解释类型和集成机制的最佳组合。在本文中，我们概述了从人类解释中学习的不同方法，并讨论了不同的因素，这些因素可以告知决定哪种方法为特定用例选择。
Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data. Adding to these empirical findings, similarity with the process of human learning makes learning from explanations a promising way to establish a fruitful human-machine interaction. Several methods have been proposed for improving natural language processing (NLP) models with human explanations, that rely on different explanation types and mechanism for integrating these explanations into the learning process. These methods are rarely compared with each other, making it hard for practitioners to choose the best combination of explanation type and integration mechanism for a specific use-case. In this paper, we give an overview of different methods for learning from human explanations, and discuss different factors that can inform the decision of which method to choose for a specific use-case.
