@inproceedings{du_aacl2020,
    title = "Leveraging Structured Metadata for Improving Question Answering on the Web",
    author = "Du, Xinya  and
      Fourney, Adam and
      Sim, Robert and
      Cardie, Claire and
      Bennett, Paul  and
      Awadallah, Ahmed Hassan",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.55",
    pages = "551--556",
    abstract = "We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding{'}s advantage over other strategies for encoding the metadata.",
}