@inproceedings{sen-etal-2021-expanding,
    title = "Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection",
    author = "Sen, Priyanka  and
      Oliya, Armin  and
      Saffari, Amir",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-main.694/",
    doi = "10.18653/v1/2021.emnlp-main.694",
    pages = "8805--8812",
    abstract = "End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable. Previous implementations of this technique (Cohen et al, 2020) have focused on single-entity questions using a relation following operation. In this paper, we propose a model that explicitly handles multiple-entity questions by implementing a new intersection operation, which identifies the shared elements between two sets of entities. We find that introducing intersection improves performance over a baseline model on two datasets, WebQuestionsSP (69.6{\%} to 73.3{\%} Hits@1) and ComplexWebQuestions (39.8{\%} to 48.7{\%} Hits@1), and in particular, improves performance on questions with multiple entities by over 14{\%} on WebQuestionsSP and by 19{\%} on ComplexWebQuestions."
}