Controlling the Imprint of Passivization and Negation in Contextualized Representations

Abstract

Contextualized word representations encode rich information about syntax and semantics, alongside specificities of each context of use. While contextual variation does not always reflect actual meaning shifts, it can still reduce the similarity of embeddings for word instances having the same meaning. We explore the imprint of two specific linguistic alternations, namely passivization and negation, on the representations generated by neural models trained with two different objectives: masked language modeling and translation. Our exploration methodology is inspired by an approach previously proposed for removing societal biases from word vectors. We show that passivization and negation leave their traces on the representations, and that neutralizing this information leads to more similar embeddings for words that should preserve their meaning in the transformation. We also find clear differences in how the respective features generalize across datasets.

The Self-Contained Negation Test Set

“…However, it has been established that the presence of negation can be detected in contextual representations. Celikkanat et al (2020) thus find "traces" of negation on the negated verb, its subject, its object. Moreover, the extent to which negation is diffused in contextual representations follows syntactic constraints: Kletz et al (2023) show that the presence of negation in contextual representations is stronger for tokens within the scope of negation, this effect being visible even when controlling for the distance between the token and negation.…”