Vaclav Cvicek


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Making Transformers Solve Compositional Tasks
Santiago Ontanon | Joshua Ainslie | Zachary Fisher | Vaclav Cvicek
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Several studies have reported the inability of Transformer models to generalize compositionally, a key type of generalization in many NLP tasks such as semantic parsing. In this paper we explore the design space of Transformer models showing that the inductive biases given to the model by several design decisions significantly impact compositional generalization. We identified Transformer configurations that generalize compositionally significantly better than previously reported in the literature in many compositional tasks. We achieve state-of-the-art results in a semantic parsing compositional generalization benchmark (COGS), and a string edit operation composition benchmark (PCFG).


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ETC: Encoding Long and Structured Inputs in Transformers
Joshua Ainslie | Santiago Ontanon | Chris Alberti | Vaclav Cvicek | Zachary Fisher | Philip Pham | Anirudh Ravula | Sumit Sanghai | Qifan Wang | Li Yang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, “Extended Transformer Construction” (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a “Contrastive Predictive Coding” (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs.