Sureshkumar Vivek Kalyan


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Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings
Sureshkumar Vivek Kalyan | Sam Witteveen | Martin Andrews
Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)

Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. This year, to refocus the Textgraphs Shared Task on the problem of gathering relevant statements (rather than solely finding a single ‘correct path’), the WorldTree dataset was augmented with expert ratings of ‘relevance’ of statements to each overall explanation. Our system, which achieved second place on the Shared Task leaderboard, combines initial statement retrieval; language models trained to predict the relevance scores; and ensembling of a number of the resulting rankings. Our code implementation is made available at

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Shared Task 1 System Description : Exploring different approaches for multilingual tasks
Sureshkumar Vivek Kalyan | Tan Paul | Tan Shaun | Martin Andrews
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

The aim of the CASE 2021 Shared Task 1 was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these subtasks being evaluated separately in each test language. Our submission contained entries in all of the subtasks, and the scores obtained validated our research finding : That the multilingual element of the tasks should be embraced, so that modeling and training regimes use the multilingual nature of the tasks to their mutual benefit, rather than trying to tackle the different languages separately.