Linear Attention Depth and Semantic Similarity on GLUE STS-B Versus Standard Transformer Distillation

Abstract

Abstract: Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in which each word in sentence A attends to all words in sentence B and vice versa. As a result, computing the similarity between a query sentence and a set of candidate sentences, requires the propagation of all query-candidate sentence-pairs throughout a stack of cross-attention layers. This exhaustive process becomes computationally prohibitive when the number of candidate sentences is large. In contrast, sentence em

Research Question

How does the depth of linear attention layers impact semantic textual similarity scores on the GLUE STS-B task compared to standard Transformer distillation methods?

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