32 citations · 39 across the 7 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
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…
Unification-based Reconstruction of Multi-hop Explanations for Science Questions
Marco Valentino, Mokanarangan Thayaparan, André Freitas
This paper presents a novel framework for reconstructing multi-hop explanations in science Question Answering (QA). While existing approaches for multi-hop reasoning build explanat…
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks
Mokanarangan Thayaparan, Marco Valentino, Viktor Schlegel +1
Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. However, compl…