  VAST, the Valence-Assessing Semantics Test, is a novel intrinsic evaluation
task for contextualized word embeddings (CWEs). VAST uses valence, the
association of a word with pleasantness, to measure the correspondence of
word-level LM semantics with widely used human judgments, and examines the
effects of contextualization, tokenization, and LM-specific geometry. Because
prior research has found that CWEs from GPT-2 perform poorly on other intrinsic
evaluations, we select GPT-2 as our primary subject, and include results
showing that VAST is useful for 7 other LMs, and can be used in 7 languages.
GPT-2 results show that the semantics of a word incorporate the semantics of
context in layers closer to model output, such that VAST scores diverge between
our contextual settings, ranging from Pearson's rho of .55 to .77 in layer 11.
We also show that multiply tokenized words are not semantically encoded until
layer 8, where they achieve Pearson's rho of .46, indicating the presence of an
encoding process for multiply tokenized words which differs from that of singly
tokenized words, for which rho is highest in layer 0. We find that a few
neurons with values having greater magnitude than the rest mask word-level
semantics in GPT-2's top layer, but that word-level semantics can be recovered
by nullifying non-semantic principal components: Pearson's rho in the top layer
improves from .32 to .76. After isolating semantics, we show the utility of
VAST for understanding LM semantics via improvements over related work on four
word similarity tasks, with a score of .50 on SimLex-999, better than the
previous best of .45 for GPT-2. Finally, we show that 8 of 10 WEAT bias tests,
which compare differences in word embedding associations between groups of
words, exhibit more stereotype-congruent biases after isolating semantics,
indicating that non-semantic structures in LMs also mask biases.
