@inproceedings{pei-etal-2023-use,
    title = "When to Use What: An In-Depth Comparative Empirical Analysis of {O}pen{IE} Systems for Downstream Applications",
    author = "Pei, Kevin  and
      Jindal, Ishan  and
      Chang, Kevin Chen-Chuan  and
      Zhai, ChengXiang  and
      Li, Yunyao",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.53",
    doi = "10.18653/v1/2023.acl-long.53",
    pages = "929--949",
    abstract = "Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one{'}s applications. We demonstrate the applicability of our recommendations on a downstream Complex QA application.",
}

Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one{'}s applications. We demonstrate the applicability of our recommendations on a downstream Complex QA application.
Pei, Kevin Jindal, Ishan Chang, Kevin Chen-Chuan Zhai, ChengXiang Li, Yunyao
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications