Feeding What You Need by Understanding What You Learned
Xiaoqiang Wang, Bang Liu, Fangli Xu, Bo Long, Siliang Tang, Lingfei Wu

TL;DR
This paper introduces an explainable framework to assess model capabilities in MRC, enabling data-driven curriculum learning that significantly enhances performance and training efficiency.
Contribution
It proposes a novel multi-dimensional capability assessment framework and a curriculum learning strategy that leverages model understanding to improve MRC training.
Findings
Achieved up to 11.22% improvement in EM
Achieved up to 8.71% improvement in F1
Enhanced training efficiency through capability-based curriculum
Abstract
Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-size models and corpus to improve the performance evaluated by metrics such as Exact Match () and . However, such a paradigm lacks sufficient interpretation to model capability and can not efficiently train a model with a large corpus. In this paper, we argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data based on its learning status. Specifically, we design an MRC capability assessment framework that assesses model capabilities in an explainable and multi-dimensional manner. Based on it, we further uncover and disentangle the connections between various data properties and model performance. Finally, to verify the…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Text Readability and Simplification
