@inproceedings{j-wang-etal-2022-globalpointer,
    title = "A {G}lobal{P}ointer based Robust Approach for Information Extraction from Dialog Transcripts",
    author = "Wang, Yanbo J.  and
      Chen, Sheng  and
      Cai, Hengxing  and
      Wei, Wei  and
      Yan, Kuo  and
      Sun, Zhe  and
      Qin, Hui  and
      Li, Yuming  and
      Cai, Xiaochen",
    editor = "Ou, Zhijian  and
      Feng, Junlan  and
      Li, Juanzi",
    booktitle = "Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, Beijing (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.seretod-1.2",
    doi = "10.18653/v1/2022.seretod-1.2",
    pages = "13--18",
    abstract = "With the widespread popularisation of intelligent technology, task-based dialogue systems (TOD) are increasingly being applied to a wide variety of practical scenarios. As the key tasks in dialogue systems, named entity recognition and slot filling play a crucial role in the completeness and accuracy of information extraction. This paper is an evaluation paper for Sere-TOD 2022 Workshop challenge (Track 1 Information extraction from dialog transcripts). We proposed a multi-model fusion approach based on GlobalPointer, combined with some optimisation tricks, finally achieved an entity F1 of 60.73, an entity-slot-value triple F1 of 56, and an average F1 of 58.37, and got the highest score in SereTOD 2022 Workshop challenge",
}