Search Results for author: Mokanarangan Thayaparan

Found 16 papers, 7 papers with code

TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration

1 code implementation NAACL (TextGraphs) 2021 Peter Jansen, Mokanarangan Thayaparan, Marco Valentino, Dmitry Ustalov

While previous editions of this shared task aimed to evaluate explanatory completeness – finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations.

TextGraphs 2022 Shared Task on Natural Language Premise Selection

1 code implementation COLING (TextGraphs) 2022 Marco Valentino, Deborah Ferreira, Mokanarangan Thayaparan, André Freitas, Dmitry Ustalov

In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems.

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

Going Beyond Approximation: Encoding Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers

no code implementations5 Aug 2022 Mokanarangan Thayaparan, Marco Valentino, André Freitas

Integer Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language.

Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration

1 code implementation25 Jul 2021 Marco Valentino, Mokanarangan Thayaparan, Deborah Ferreira, André Freitas

Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference.

Multi-hop Question Answering Natural Language Inference +1

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.

ExplanationLP: Abductive Reasoning for Explainable Science Question Answering

no code implementations25 Oct 2020 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.

Answer Selection Multiple-choice +1

A Survey on Explainability in Machine Reading Comprehension

no code implementations1 Oct 2020 Mokanarangan Thayaparan, Marco Valentino, André Freitas

This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC).

Machine Reading Comprehension

Case-Based Abductive Natural Language Inference

no code implementations COLING 2022 Marco Valentino, Mokanarangan Thayaparan, André Freitas

Most of the contemporary approaches for multi-hop Natural Language Inference (NLI) construct explanations considering each test case in isolation.

Natural Language Inference Question Answering

Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks

no code implementations WS 2019 Mokanarangan Thayaparan, Marco Valentino, Viktor Schlegel, Andre Freitas

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.

Multi-hop Question Answering Question Answering +1

Cannot find the paper you are looking for? You can Submit a new open access paper.