Papers with supervised classification task
Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection (2022.aacl-main)
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| Challenge: | Existing approaches to detect hate speech are expensive and time-consuming . a new approach allows for flexible learning of neighborhood information . |
| Approach: | They propose a method that allows flexible modeling of neighbors retrieved from a resource-rich corpus to learn the amount of transfer. |
| Outcome: | The proposed training strategy improves on low-resource hate speech corpora over baselines. |
Self-supervised Regularization for Text Classification (2021.tacl-1)
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| Challenge: | Text classification models are prone to overfitting when limited texts are available for training. |
| Approach: | They propose a data-dependent regularization approach based on self-supervised learning . they define auxiliary tasks on input data without using human-provided labels . |
| Outcome: | Experiments on 17 text classification datasets demonstrate the effectiveness of the proposed method. |
Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution (2020.coling-main)
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| Challenge: | Existing Variational autoencoders are limited by the assumed Gaussianity of the underlying probability distributions in the latent space. |
| Approach: | They propose a probabilistic autoencoding framework to deal with a supervised authorship attribution task. |
| Outcome: | The proposed method outperforms existing methods on an Amazon review dataset. |
Event Causality Identification via Derivative Prompt Joint Learning (2022.coling-1)
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| Challenge: | Existing methods for event causality identification lack annotated data, and they lack the ability to identify explicit and implicit causality. |
| Approach: | They propose a derivative prompt joint learning model which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. |
| Outcome: | The proposed model can identify explicit and implicit causality on two benchmark datasets and it has great advantages over previous methods. |
Aligning Predictive Uncertainty with Clarification Questions in Grounded Dialog (2023.findings-emnlp)
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| Challenge: | Previous work bases the timing of questions on supervised models learned from interactions between humans. |
| Approach: | They propose to ground the need for questions in the acting agent's predictive uncertainty by using the T5 encoder-decoder architecture to solve a Minecraft Collaborative Building task. |
| Outcome: | The proposed model can detect ambiguous instructions and predict responses better than previous models. |