Scaling Distilled Sentence Embedding Models with Linear Attention Beyond 512 Tokens on GLUE STS-B

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 do distilled sentence embedding models with linear attention layers scale in terms of training convergence speed and final Pearson correlation on the GLUE STS-B task when increasing sequence length beyond 512 tokens?

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