M
Mokanarangan Thayaparan
Researcher at University of Manchester
Publications - 21
Citations - 99
Mokanarangan Thayaparan is an academic researcher from University of Manchester. The author has contributed to research in topics: Inference & Question answering. The author has an hindex of 5, co-authored 20 publications receiving 65 citations. Previous affiliations of Mokanarangan Thayaparan include University of Moratuwa.
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A Survey on Explainability in Machine Reading Comprehension.
TL;DR: This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC), and presents the evaluation methodologies to assess the performance of explainable systems.
Proceedings ArticleDOI
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks
TL;DR: Document Graph Network is proposed, a message passing architecture for the identification of supporting facts over a graph-structured representation of text that obtains competitive results when compared to a reading comprehension baseline operating on raw text, confirming the relevance of structured representations for supporting multi-hop reasoning.
Proceedings ArticleDOI
Unification-based Reconstruction of Multi-hop Explanations for Science Questions.
TL;DR: This article proposed a framework for reconstructing multi-hop explanations in science Question Answering, which ranks a set of atomic facts by integrating lexical relevance with the notion of unification power, estimated analysing explanations for similar questions in the corpus.
Proceedings ArticleDOI
TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration
TL;DR: This edition of the shared task makes use of a large set of approximately 250k manual explanatory relevancy ratings that augment the 2020 shared task data, and performs a detailed analysis of participating systems, evaluating various aspects involved in the multi-hop inference process.
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Unification-based Reconstruction of Explanations for Science Questions.
TL;DR: A framework to reconstruct explanations for multiple choices science questions through explanation-centred corpora that achieves competitive results when compared to state-of-the-art Transformers, yet possessing the property of being scalable to large explanatory knowledge bases.