@inproceedings{ruan-etal-2022-pingan,
    title = "{PINGAN}{\_}{AI} at {S}em{E}val-2022 Task 9: Recipe knowledge enhanced model applied in Competence-based Multimodal Question Answering",
    author = "Ruan, Zhihao  and
      Hou, Xiaolong  and
      Jiang, Lianxin",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.semeval-1.179/",
    doi = "10.18653/v1/2022.semeval-1.179",
    pages = "1274--1279",
    abstract = "This paper describes our system used in the SemEval-2022 Task 09: R2VQ - Competence-based Multimodal Question Answering. We propose a knowledge-enhanced model for predicting answer in QA task, this model use BERT as the backbone. We adopted two knowledge-enhanced methods in this model: the knowledge auxiliary text method and the knowledge embedding method. We also design an answer extraction task pipeline, which contains an extraction-based model, an automatic keyword labeling module, and an answer generation module. Our system ranked 3rd in task 9 and achieved an exact match score of 78.21 and a word-level F1 score of 82.62."
}