Papers with global-local attention
ETC: Encoding Long and Structured Inputs in Transformers (2020.emnlp-main)
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Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang
| Challenge: | Existing models for natural language processing (NLP) have been challenging to scale attention to longer inputs. |
| Approach: | They propose an extended Transformer construction architecture that scales attention to longer inputs by combining global-local attention with relative position encodings and a "Contrastive Predictive Coding" objective. |
| Outcome: | The proposed architecture scales attention to longer inputs and encodes structured inputs. |