@inproceedings{dsouza-etal-2019-team,
    title = "Team {SVM}rank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions",
    author = {D{'}Souza, Jennifer  and
      Mulang{'}, Isaiah Onando  and
      Auer, S{\"o}ren},
    editor = "Ustalov, Dmitry  and
      Somasundaran, Swapna  and
      Jansen, Peter  and
      Glava{\v{s}}, Goran  and
      Riedl, Martin  and
      Surdeanu, Mihai  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D19-5312/",
    doi = "10.18653/v1/D19-5312",
    pages = "90--100",
    abstract = "The TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration (MIER-19) tackles explanation generation for answers to elementary science questions. It builds on the AI2 Reasoning Challenge 2018 (ARC-18) which was organized as an advanced question answering task on a dataset of elementary science questions. The ARC-18 questions were shown to be hard to answer with systems focusing on surface-level cues alone, instead requiring far more powerful knowledge and reasoning. To address MIER-19, we adopt a hybrid pipelined architecture comprising a featurerich learning-to-rank (LTR) machine learning model, followed by a rule-based system for reranking the LTR model predictions. Our system was ranked fourth in the official evaluation, scoring close to the second and third ranked teams, achieving 39.4{\%} MAP."
}