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arXiv · 2104.06045

Structural analysis of an all-purpose question answering model

Vincent Micheli·Quentin Heinrich·François Fleuret·Wacim Belblidia
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Abstract

Attention is a key component of the now ubiquitous pre-trained language models. By learning to focus on relevant pieces of information, these Transformer-based architectures have proven capable of tackling several tasks at once and sometimes even surpass their single-task counterparts. To better understand this phenomenon, we conduct a structural analysis of a new all-purpose question answering model that we introduce. Surprisingly, this model retains single-task performance even in the absence of a strong transfer effect between tasks. Through attention head importance scoring, we observe that attention heads specialize in a particular task and that some heads are more conducive to learning than others in both the multi-task and single-task settings.

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Vincent Micheli, Quentin Heinrich, François Fleuret, Wacim Belblidia. 2021-04-13. Structural analysis of an all-purpose question answering model. https://arxiv.org/abs/2104.06045

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