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arXiv · 2101.00389

Multitask Learning for Class-Imbalanced Discourse Classification

Alexander Spangher·Jonathan May·Sz-rung Shiang·Lingjia Deng
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Abstract

Small class-imbalanced datasets, common in many high-level semantic tasks like discourse analysis, present a particular challenge to current deep-learning architectures. In this work, we perform an extensive analysis on sentence-level classification approaches for the News Discourse dataset, one of the largest high-level semantic discourse datasets recently published. We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks, due in part to label corrections across tasks, which improve performance for underrepresented classes. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in such a setting.

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Alexander Spangher, Jonathan May, Sz-rung Shiang, Lingjia Deng. 2021-01-02. Multitask Learning for Class-Imbalanced Discourse Classification. https://arxiv.org/abs/2101.00389

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