TTCB System Description to a Shared Task on Implicit and Underspecified Language 2021

Peratham Wiriyathammabhum


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 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.
Anthology ID:
2021.unimplicit-1.8
Volume:
Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | unimplicit
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
64–70
Language:
URL:
https://aclanthology.org/2021.unimplicit-1.8
DOI:
10.18653/v1/2021.unimplicit-1.8
Bibkey:
Cite (ACL):
Peratham Wiriyathammabhum. 2021. TTCB System Description to a Shared Task on Implicit and Underspecified Language 2021. In Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language, pages 64–70, Online. Association for Computational Linguistics.
Cite (Informal):
TTCB System Description to a Shared Task on Implicit and Underspecified Language 2021 (Wiriyathammabhum, unimplicit 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.unimplicit-1.8.pdf