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arXiv AI By Tian Tan, Eduardo Blanco

Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding

Read the original on arXiv AI →

The paper introduces NegCue, a large-scale dataset of 1.8 million samples that includes single-word, multi-word, and affixal negation cues, totaling over 200 unique forms. The authors pre-train encoder-only language models and large language models on this dataset to study how different negation types influence understanding. Experiments on five downstream benchmarks reveal that affixal negations provide the most significant performance gains, whereas single-word negations yield modest improvements, and that additional pre-training benefits both model types.

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