@inproceedings{duan-etal-2020-machine,
    title = "Machine Reasoning: Technology, Dilemma and Future",
    author = "Duan, Nan  and
      Tang, Duyu  and
      Zhou, Ming",
    editor = "Villavicencio, Aline  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-tutorials.1",
    doi = "10.18653/v1/2020.emnlp-tutorials.1",
    pages = "1--6",
    abstract = "Machine reasoning research aims to build interpretable AI systems that can solve problems or draw conclusions from what they are told (i.e. facts and observations) and already know (i.e. models, common sense and knowledge) under certain constraints. In this tutorial, we will (1) describe the motivation of this tutorial and give our definition on machine reasoning; (2) introduce typical machine reasoning frameworks, including symbolic reasoning, probabilistic reasoning, neural-symbolic reasoning and neural-evidence reasoning, and show their successful applications in real-world scenarios; (3) talk about the dilemma between black-box neural networks with state-of-the-art performance and machine reasoning approaches with better interpretability; (4) summarize the content of this tutorial and discuss possible future directions.",
}