@inproceedings{chen-etal-2020-finding,
    title = "Finding {F}riends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory",
    author = "Chen, Hannah  and
      Ji, Yangfeng  and
      Evans, David",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.findings-emnlp.426/",
    doi = "10.18653/v1/2020.findings-emnlp.426",
    pages = "4741--4751",
    abstract = "Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a pretrained BERT model, and find that the automatically-enhanced training sets result in more accurate models."
}