Papers with macro-average F1 score
An Open Dataset and Model for Language Identification (2023.acl-short)
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| Challenge: | Existing LID systems perform poorly on low-resource languages, causing 'representation washing', where the community is given a false view of the actual progress of low-source NLP. |
| Approach: | They propose a model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033% across 201 languages, outperforming previous work. |
| Outcome: | The proposed model outperforms existing models and datasets on 201 languages and a false positive rate of 0.033%. |
Sequential Path Signature Networks for Personalised Longitudinal Language Modeling (2023.findings-acl)
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| Challenge: | Current work on low-dimensional static user representations or more importantly on dynamic user representation is limited. |
| Approach: | They propose to integrate path signatures from rough path theory into neural sequential models by integrating contextual neural representations and recursive neural networks. |
| Outcome: | The proposed model outperforms state-of-the-art models on macro-average F1 score on two available datasets and outperformed previous models which only have access to historical posts. |