Papers by Vaclav Cvicek
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. |
Making Transformers Solve Compositional Tasks (2022.acl-long)
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| Challenge: | Several studies have reported the inability of Transformer models to generalize compositionally . a key aspect of natural language is the ability to learn basic primitives . |
| Approach: | They propose to use Transformers to generalize compositionally in a large range of tasks . they find that Transformers generalize significantly better than previous models . |
| Outcome: | The proposed models generalize compositionally significantly better than previous models . a set of 12 datasets shows that the proposed models can be improved . |