Papers with long-tail distribution
Harmless Transfer Learning for Item Embeddings (2022.findings-naacl)
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| Challenge: | Existing approaches to learn item embeddings for categorical features are limited by the frequency of items in real-world. |
| Approach: | They propose a method that transfers knowledge from frequent items to rare items by introducing an auxiliary transfer loss. |
| Outcome: | The proposed framework significantly boosts the performance on a variety of NLP and recommendation system tasks. |
CoVariance-based Causal Debiasing for Entity and Relation Extraction (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition and Relation Extraction are key tasks of Information Extraction. |
| Approach: | They propose a causal framework called c ovariance and variance optimization framework (OVO) to optimize feature representations and conduct general debiasing. |
| Outcome: | The proposed framework minimizes characterizing features’ covariance for alleviating selection and distribution bias and enhances feature representation in the feature space. |
Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)
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| Challenge: | a large number of medical encounters need to be coded everyday due to long document sets and large label set. |
| Approach: | They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents . |
| Outcome: | The proposed model outperforms prior best model and multilingual Transformer model on a widely used dataset in the medical domain. |