5 citations · 10 across the 7 of their papers we have counts for
6 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…
Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration
Marco Valentino, Mokanarangan Thayaparan, Deborah Ferreira +1
Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference. In this context, large…
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…
Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text
Deborah Ferreira, Andre Freitas
Mathematical text is written using a combination of words and mathematical expressions. This combination, along with a specific way of structuring sentences makes it challenging fo…