Approximate Attributions for Off-the-Shelf Siamese Transformers
Lucas Möller Dmitry Nikolaev Sebastian Padó
Siamese encoders such as sentence transformers are among the least understood
deep models. Established attribution methods cannot tackle this model class
since it compares two inputs rather than processing a single one. To address
this gap, we have recently proposed an attribution method specifically for
Siamese encoders (M\"oller et al., 2023). However, it requires models to be
adjusted and fine-tuned and therefore cannot be directly applied to
off-the-shelf models. In this work, we reassess these restrictions and propose
(i) a model with exact attribution ability that retains the original model's
predictive performance and (ii) a way to compute approximate attributions for
off-the-shelf models. We extensively compare approximate and exact attributions
and use them to analyze the models' attendance to different linguistic aspects.
We gain insights into which syntactic roles Siamese transformers attend to,
confirm that they mostly ignore negation, explore how they judge semantically
opposite adjectives, and find that they exhibit lexical bias.