Search Results for author: Julia Rozanova

Found 7 papers, 2 papers with code

To be or not to be an Integer? Encoding Variables for Mathematical Text

no code implementations Findings (ACL) 2022 Deborah Ferreira, Mokanarangan Thayaparan, Marco Valentino, Julia Rozanova, Andre Freitas

The application of Natural Language Inference (NLI) methods over large textual corpora can facilitate scientific discovery, reducing the gap between current research and the available large-scale scientific knowledge.

Natural Language Inference

Decomposing Natural Logic Inferences in Neural NLI

no code implementations15 Dec 2021 Julia Rozanova, Deborah Ferreira, Marco Valentino, Mokanrarangan Thayaparan, Andre Freitas

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 crucial semantic features central to natural logic: monotonicity and concept inclusion.

Decision Making

Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?

1 code implementation17 Sep 2021 Julia Rozanova, Deborah Ferreira, Krishna Dubba, Weiwei Cheng, Dell Zhang, Andre Freitas

Even though BERT and similar pre-trained language models have excelled in several NLP tasks, their use has not been widely explored for the UI grounding domain.

Supporting Context Monotonicity Abstractions in Neural NLI Models

no code implementations ACL (NALOMA, IWCS) 2021 Julia Rozanova, Deborah Ferreira, Mokanarangan Thayaparan, Marco Valentino, André Freitas

Natural language contexts display logical regularities with respect to substitutions of related concepts: these are captured in a functional order-theoretic property called monotonicity.

Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference

no code implementations7 May 2021 Mokanarangan Thayaparan, Marco Valentino, Deborah Ferreira, Julia Rozanova, André Freitas

This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization.

Multi-hop Question Answering Natural Language Inference +3

Does My Representation Capture X? Probe-Ably

1 code implementation ACL 2021 Deborah Ferreira, Julia Rozanova, Mokanarangan Thayaparan, Marco Valentino, André Freitas

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 models.

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