Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning
Abstract: Entity disambiguation (ED) is crucial in natural language processing (NLP) for tasks such as question-answering and information extraction. A major challenge in ED is handling overshadowed entities—uncommon entities sharing mention surfaces with common entities. The current approach to enhance performance on these entities involves reasoning over facts in a knowledge base (KB), increasing computational overhead during inference. We argue that the ED performance on overshadowed entities can be enhanced during training by addressing shortcut learning, which does not add computational overhead at inference. We propose a simple yet effective debiasing technique to prevent models from shortcut learning during training. Experiments on a range of ED datasets show that our method achieves state-of-the-art performance without compromising inference speed. Our findings suggest a new research direction for improving entity disambiguation via shortcut learning mitigation.
Paper Type: Short
Research Area: Information Extraction
Research Area Keywords: Information Extraction
Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency
Languages Studied: English
Submission Number: 2184
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