activity
20192025
most citedA Survey on Explainability in Machine Reading Comprehension

32 citations · 32 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CL2025

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages

Imalsha Puranegedara, Themira Chathumina, Nisal Ranathunga +3

Large Language Models (LLMs) excel in English, but their performance degrades significantly on low-resource languages (LRLs) due to English-centric training. While methods like Lan…

cs.CL2021

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…

cs.CL2021

Switching Contexts: Transportability Measures for NLP

Guy Marshall, Mokanarangan Thayaparan, Philip Osborne +1

This paper explores the topic of transportability, as a sub-area of generalisability. By proposing the utilisation of metrics based on well-established statistics, we are able to e…

cs.LG2021

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…

cs.AI2020

ExplanationLP: Abductive Reasoning for Explainable Science Question Answering

Mokanarangan Thayaparan, Marco Valentino, André Freitas

We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains. This paper frames question answe…

cs.CL202032 cited

A Survey on Explainability in Machine Reading Comprehension

Mokanarangan Thayaparan, Marco Valentino, André Freitas

This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference chal…