Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings

Abstract

Models based on the transformer architecture, such as BERT, have marked a crucial step for- ward in the field of Natural Language Pro- cessing. Importantly, they allow the creation of word embeddings that capture important semantic information about words in context. However, as single entities, these embeddings are difficult to interpret and the models used to create them have been described as opaque. Binder and colleagues proposed an intuitive embedding space where each dimension is based on one of 65 core semantic features. Un- fortunately, the space only exists for a small data-set of 535 words, limiting its uses. Pre- vious work (Utsumi, 2018, 2020; Turton et al., 2020) has shown that Binder features can be derived from static embeddings and success- fully extrapolated to a large new vocabulary. Taking the next step, this paper demonstrates that Binder features can be derived from the BERT embedding space. This provides two things; (1) semantic feature values derived from contextualised word embeddings and (2) insights into how semantic features are repre- sented across the different layers of the BERT model