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        <title>ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking</title>
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    <name type="personal">
        <namePart type="given">Pratyay</namePart>
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            <title>Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)</title>
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            <namePart type="given">Dmitry</namePart>
            <namePart type="family">Ustalov</namePart>
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        <name type="personal">
            <namePart type="given">Swapna</namePart>
            <namePart type="family">Somasundaran</namePart>
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            <namePart type="given">Peter</namePart>
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    <abstract>In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-the-art language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set.</abstract>
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%0 Conference Proceedings
%T ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking
%A Banerjee, Pratyay
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Jansen, Peter
%Y Glavaš, Goran
%Y Riedl, Martin
%Y Surdeanu, Mihai
%Y Vazirgiannis, Michalis
%S Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F banerjee-2019-asu
%X In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-the-art language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set.
%R 10.18653/v1/D19-5310
%U https://aclanthology.org/D19-5310
%U https://doi.org/10.18653/v1/D19-5310
%P 78-84

Markdown (Informal)

[ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking](https://aclanthology.org/D19-5310) (Banerjee, TextGraphs 2019)

• ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking (Banerjee, TextGraphs 2019)

ACL

• Pratyay Banerjee. 2019. ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 78–84, Hong Kong. Association for Computational Linguistics.