Papers by Daniel Campos
Open Domain Web Keyphrase Extraction Beyond Language Modeling (D19-1)
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| Challenge: | Recent neural methods for keyphrase extraction are mostly observed in documents originating from the scientific domain. |
| Approach: | They develop a neural keyphrase extraction model that goes beyond language understanding to handle the variations of domain and content quality. |
| Outcome: | The proposed model can handle the variations of domain and content quality without restriction of the domain, quality, nor content of the documents. |
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models (2022.emnlp-main)
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Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh
| Challenge: | Pre-trained Transformer models provide robust language representations which can be specialized on various tasks. |
| Approach: | They propose an efficient pruning method based on approximate second-order information that allows pruning weight blocks to be used for pruning. |
| Outcome: | The proposed method is the first to be applied at the BERT scale and significantly pushes the boundaries of the current sparse models with respect to all metrics: model size, inference speed and task accuracy. |
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)
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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
| Challenge: | XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios . |
| Approach: | They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora. |
| Outcome: | The proposed dataset is labeled in English and includes only natural language understanding tasks. |