TTCB System Description to a Shared Task on Implicit and Underspecified Language 2021
Peratham Wiriyathammabhum · 2021
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 68.45% test accuracy (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.