3 citations · 5 across the 10 of their papers we have counts for
5 papers · 1 filter
Decomposing Natural Logic Inferences in Neural NLI
Julia Rozanova, Deborah Ferreira, Marco Valentino +2
In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucia…
Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?
Julia Rozanova, Deborah Ferreira, Krishna Dubba +3
Models designed for intelligent process automation are required to be capable of grounding user interface elements. This task of interface element grounding is centred on linking i…
Supporting Context Monotonicity Abstractions in Neural NLI Models
Julia Rozanova, Deborah Ferreira, Mokanarangan Thayaparan +2
Natural language contexts display logical regularities with respect to substitutions of related concepts: these are captured in a functional order-theoretic property called monoton…
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
Mokanarangan Thayaparan, Marco Valentino, Deborah Ferreira +2
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differenti…
Does My Representation Capture X? Probe-Ably
Deborah Ferreira, Julia Rozanova, Mokanarangan Thayaparan +2
Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural mode…