@inproceedings{vivek-kalyan-etal-2021-textgraphs,
    title = "Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings",
    author = "Vivek Kalyan, Sureshkumar  and
      Witteveen, Sam  and
      Andrews, Martin",
    editor = "Panchenko, Alexander  and
      Malliaros, Fragkiskos D.  and
      Logacheva, Varvara  and
      Jana, Abhik  and
      Ustalov, Dmitry  and
      Jansen, Peter",
    booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
    month = jun,
    year = "2021",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.textgraphs-1.20/",
    doi = "10.18653/v1/2021.textgraphs-1.20",
    pages = "176--180",
    abstract = "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 {\textquoteleft}correct path'), the WorldTree dataset was augmented with expert ratings of {\textquoteleft}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 \url{https://github.com/mdda/worldtree_corpus/tree/textgraphs_2021}"
}