CogLTX: Applying BERT to Long Texts

By Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang. 2020

161 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Han
2. Xiao Liu
3. Zhiyuan Liu
4. Ning Ding
5. Xipeng Qiu
6. Minlie Huang
7. Yuxian Gu
8. Zhengyan Zhang

Citations in the corpus

• Pre-Trained Models: Past, Present and Future (2021, methodology): “The success of CogQA and CogLTX may provide some thoughts on this challenge.”
• Blockwise Self-Attention for Long Document Understanding (2019, positive): “(Ding et al., 2020) presents an alternative idea based on cognitive theory to construct a workingmemory by identifying key sentences, which enables multi-step reasoning.”
• REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling (2026, unknown): “ToBERT (Pappagari et al., 2019) and CogLTX (Ding et al., 2020) use this recipe for supervised classification over documents of a few hundred to a few thousand words.”