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Beyond Labels: Empowering Human with Natural Language Explanations through a Novel Active-Learning Architecture

22 May 2023-
TL;DR: In this paper , a data diversity-based active learning framework is proposed to support and reduce human annotations of both labels and explanations in low-resource scenarios, which can explicitly generate natural language explanations for the prediction model and for assisting humans' decision-making in real world.
Abstract: Data annotation is a costly task; thus, researchers have proposed low-scenario learning techniques like Active-Learning (AL) to support human annotators; Yet, existing AL works focus only on the label, but overlook the natural language explanation of a data point, despite that real-world humans (e.g., doctors) often need both the labels and the corresponding explanations at the same time. This work proposes a novel AL architecture to support and reduce human annotations of both labels and explanations in low-resource scenarios. Our AL architecture incorporates an explanation-generation model that can explicitly generate natural language explanations for the prediction model and for assisting humans' decision-making in real-world. For our AL framework, we design a data diversity-based AL data selection strategy that leverages the explanation annotations. The automated AL simulation evaluations demonstrate that our data selection strategy consistently outperforms traditional data diversity-based strategy; furthermore, human evaluation demonstrates that humans prefer our generated explanations to the SOTA explanation-generation system.

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Trending Questions (1)
What papers are using active learning beyond just providing labels?

The provided paper does not mention any other papers that use active learning beyond just providing labels. The paper focuses on proposing a novel active learning architecture that incorporates an explanation-generation model to support and reduce human annotations of both labels and explanations.