Papers with localness modeling
Mask Attention Networks: Rethinking and Strengthen Transformer (2021.naacl-main)
Copied to clipboard
Zhihao Fan, Yeyun Gong, Dayiheng Liu, Zhongyu Wei, Siyuan Wang, Jian Jiao, Nan Duan, Ruofei Zhang, Xuanjing Huang
| Challenge: | Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. |
| Approach: | They propose to combine SAN and Feed-Forward Networks to create a dynamic mask attention network with a learnable mask matrix which can model localness adaptively. |
| Outcome: | The proposed model outperforms the original Transformer on translation and text summarization tasks. |
Porous Lattice Transformer Encoder for Chinese NER (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to integrate word boundary information into character-level Chinese NER are inefficient and lack semantic interaction. |
| Approach: | They propose an extension of transformer encoder that is tailored for ChineseNER to incorporate lexicons into character-level Chinese NER by lattices. |
| Outcome: | The proposed extension performs 11.4 times faster than state-of-the-art methods while retaining the rich long-term dependencies. |
Modeling Localness for Self-Attention Networks (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to model locality for self-attention networks have shown great value for capturing global dependencies. |
| Approach: | They propose to model localness for self-attention networks to capture local context . they cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. |
| Outcome: | The proposed model improves the ability to capture local context and improves accuracy. |
Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing question generation models treat input passage as a sequence-to-sequence generative task, but they are not aware of text structure. |
| Approach: | They propose to model text structure as answer position and syntactic dependency and propose a mask attention mechanism to make syntaktic structure of input passage accessible. |
| Outcome: | The proposed model outperforms the strong pre-trained model ProphetNet on a SQuAD dataset and achieves competitive results with the state-of-the-art model. |