@inproceedings{wiriyathammabhum-2021-ttcb,
    title = "{TTCB} System Description to a Shared Task on Implicit and Underspecified Language 2021",
    author = "Wiriyathammabhum, Peratham",
    editor = "Roth, Michael  and
      Tsarfaty, Reut  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.unimplicit-1.8/",
    doi = "10.18653/v1/2021.unimplicit-1.8",
    pages = "64--70",
    abstract = "In this report, we describe our transformers for text classification baseline (TTCB) submissions to a shared task on implicit and underspecified language 2021. We cast the task of predicting revision requirements in collaboratively edited instructions as text classification. We considered transformer-based models which are the current state-of-the-art methods for text classification. We explored different training schemes, loss functions, and data augmentations. Our best result of \textit{68.45{\%}} test accuracy (\textit{68.84{\%}} validation accuracy), however, consists of an XLNet model with a linear annealing scheduler and a cross-entropy loss. We do not observe any significant gain on any validation metric based on our various design choices except the MiniLM which has a higher validation F1 score and is faster to train by a half but also a lower validation accuracy score."
}