A token-classification ensemble of BanglaBERT models with rule-based post-processing detects grammatical errors in Bangla text with a reported Levenshtein distance score of 1.04 (or 1.054, the paper is inconsistent).
IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task's main aim was to identify spans to which a given text's toxicity could be attributed. The task is challenging mainly due to two constraints: the small training dataset and imbalanced class distribution. Our paper investigates two techniques, semi-supervised learning and learning with Self-Adjusting Dice Loss, for tackling these challenges. Our submitted system (ranked ninth on the leader board) consisted of an ensemble of various pre-trained Transformer Language Models trained using either of the above-proposed techniques.
fields
cs.CL 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification
A token-classification ensemble of BanglaBERT models with rule-based post-processing detects grammatical errors in Bangla text with a reported Levenshtein distance score of 1.04 (or 1.054, the paper is inconsistent).