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Conference Papers Year : 2021

Skim-Attention: Learning to Focus via Document Layout

Laura Nguyen
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  • PersonId : 1109224
Thomas Scialom
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  • PersonId : 1109225
Jacopo Staiano
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  • PersonId : 1109226

Abstract

Transformer-based pre-training techniques of text and layout have proven effective in a number of document understanding tasks. Despite this success, multimodal pre-training models suffer from very high computational and memory costs. Motivated by human reading strategies, this paper presents Skim-Attention, a new attention mechanism that takes advantage of the structure of the document and its layout. Skim-Attention only attends to the 2dimensional position of the words in a document. Our experiments show that Skim-Attention obtains a lower perplexity than prior works, while being more computationally efficient. Skim-Attention can be further combined with long-range Transformers to efficiently process long documents. We also show how Skim-Attention can be used off-the-shelf as a mask for any Pre-trained Language Model, allowing to improve their performance while restricting attention. Finally, we show the emergence of a document structure representation in Skim-Attention.
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Dates and versions

hal-03333889 , version 1 (03-09-2021)

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Laura Nguyen, Thomas Scialom, Jacopo Staiano, Benjamin Piwowarski. Skim-Attention: Learning to Focus via Document Layout. Findings of the 2021 Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP 2021), Nov 2021, Punta Cana, Dominican Republic. ⟨hal-03333889⟩
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