@inproceedings{mussmann-etal-2020-importance,
    title = "{O}n the {I}mportance of {A}daptive {D}ata {C}ollection for {E}xtremely {I}mbalanced {P}airwise {T}asks",
    author = "Mussmann, Stephen  and
      Jia, Robin  and
      Liang, Percy",
    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/ingest-emnlp/2020.findings-emnlp.305/",
    doi = "10.18653/v1/2020.findings-emnlp.305",
    pages = "3400--3413",
    abstract = "Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e.g., 99.99{\%} of examples are negatives). In contrast, many recent datasets heuristically choose examples to ensure label balance. We show that these heuristics lead to trained models that generalize poorly: State-of-the art models trained on QQP and WikiQA each have only 2.4{\%} average precision when evaluated on realistically imbalanced test data. We instead collect training data with active learning, using a BERT-based embedding model to efficiently retrieve uncertain points from a very large pool of unlabeled utterance pairs. By creating balanced training data with more informative negative examples, active learning greatly improves average precision to 32.5{\%} on QQP and 20.1{\%} on WikiQA."
}