Papers with macro-averaged F1-score
A Dataset for Investigating the Impact of Context for Offensive Language Detection in Tweets (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Offensive language detection is crucial in natural language processing . we investigated the importance of contextual information for detecting offensive language in tweets . |
| Approach: | They investigated the importance of contextual information for detecting offensive language in tweets . they used a Turkish tweet dataset with over 28,000 tweet-reply pairs . |
| Outcome: | The proposed model performs better with and without contextual information than with and with contextual information. |
A Multi-Task Learning Framework for Multi-Target Stance Detection (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing models fail to learn target-specific representations and are prone to overfitting. |
| Approach: | They propose a multi-task learning network to train one model on all target pairs . their results show that their proposed model outperforms the best-performing baseline by 12.39% . |
| Outcome: | The proposed model outperforms the best-performing baseline model by 12.39% in macro-averaged F1-score. |
Joint Persian Word Segmentation Correction and Zero-Width Non-Joiner Recognition Using BERT (2020.coling-main)
Copied to clipboard
| Challenge: | a set of rules are set by the regulatory body of the Persian language regarding the use of white space and ZWNJ . only a few people follow these rules in writing formal Persian, let alone the informal language. |
| Approach: | They address problems of word segmentation and zero-width non-joiner recognition in Persian . they use a macro-averaged F1 score of 92.40% on a carefully collected corpus of 500 sentences . |
| Outcome: | The proposed problem is a sequence labeling problem in Persian . it achieves a macro-averaged F1 score of 92.40% on a carefully collected corpus of 500 sentences . |
Detecting Health Advice in Medical Research Literature (2021.emnlp-main)
Copied to clipboard
| Challenge: | Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. |
| Approach: | They developed a BERT-based prediction model that can predict whether a sentence gives strong advice, weak advice, or not. |
| Outcome: | The proposed model can predict whether a sentence gives strong advice, weak advice, or not with a macro-averaged F1 score of 0.93. |