Papers with F1-macro
Efficient Nearest Neighbor Emotion Classification with BERT-whitening (2022.emnlp-main)
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| Challenge: | Existing methods for retrieval-based methods use pre-trained models for similarity search, but they perform poorly in retrieving semantically similar sentences. |
| Approach: | They propose a non-parametric emotion classification method using nearest neighbor retrieval that can be added to any pre-trained model without further training. |
| Outcome: | The proposed method improves the pre-trained model by 1.17 F1-macro on two emotion classification datasets. |
Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations (P19-1)
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| Challenge: | Existing approaches to learn from social-media conversations have been proposed to identify and contain fake news shared on social media platforms. |
| Approach: | They propose to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. |
| Outcome: | The proposed models outperform the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively. |
Theory of Mind in Freely-Told Children’s Narratives: A Classification Approach (2023.findings-acl)
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| Challenge: | Language and Theory of Mind (ToM) competences are often studied with younger children and standardized tests, but as both are social competences, data and methods with higher ecological validity are critical. |
| Approach: | They leveraged a corpus of 442 freely-told stories by Dutch children aged 4-12 to study language and ToM with NLP-tools. |
| Outcome: | The proposed method is robust relative to the complexity of the task for humans and is consistent with previous studies. |