@inproceedings{chia-etal-2020-red,
    title = "Red Dragon {AI} at {T}ext{G}raphs 2020 Shared Task : {LIT} : {LSTM}-Interleaved Transformer for Multi-Hop Explanation Ranking",
    author = "Chia, Yew Ken  and
      Witteveen, Sam  and
      Andrews, Martin",
    editor = "Ustalov, Dmitry  and
      Somasundaran, Swapna  and
      Panchenko, Alexander  and
      Malliaros, Fragkiskos D.  and
      Hulpuș, Ioana  and
      Jansen, Peter  and
      Jana, Abhik",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.textgraphs-1.14/",
    doi = "10.18653/v1/2020.textgraphs-1.14",
    pages = "115--120",
    abstract = "Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of methods that view each query-document pair in isolation, we propose the LSTM-Interleaved Transformer which incorporates cross-document interactions for improved multi-hop ranking. The LIT architecture can leverage prior ranking positions in the re-ranking setting. Our model is competitive on the current leaderboard for the TextGraphs 2020 shared task, achieving a test-set MAP of 0.5607, and would have gained third place had we submitted before the competition deadline. Our code implementation is made available at [\url{https://github.com/mdda/worldtree_corpus/tree/textgraphs_2020}](\url{https://github.com/mdda/worldtree_corpus/tree/textgraphs_2020})."
}