Estimating the Causal Effects of Natural Logic Features in
  Transformer-Based NLI Models
Julia Rozanova Marco Valentino André Freitas
Rigorous evaluation of the causal effects of semantic features on language
model predictions can be hard to achieve for natural language reasoning
problems. However, this is such a desirable form of analysis from both an
interpretability and model evaluation perspective, that it is valuable to
investigate specific patterns of reasoning with enough structure and regularity
to identify and quantify systematic reasoning failures in widely-used models.
In this vein, we pick a portion of the NLI task for which an explicit causal
diagram can be systematically constructed: the case where across two sentences
(the premise and hypothesis), two related words/terms occur in a shared
context. In this work, we apply causal effect estimation strategies to measure
the effect of context interventions (whose effect on the entailment label is
mediated by the semantic monotonicity characteristic) and interventions on the
inserted word-pair (whose effect on the entailment label is mediated by the
relation between these words). Extending related work on causal analysis of NLP
models in different settings, we perform an extensive interventional study on
the NLI task to investigate robustness to irrelevant changes and sensitivity to
impactful changes of Transformers. The results strongly bolster the fact that
similar benchmark accuracy scores may be observed for models that exhibit very
different behaviour. Moreover, our methodology reinforces previously suspected
biases from a causal perspective, including biases in favour of upward-monotone
contexts and ignoring the effects of negation markers.