Universal Transformers combine Transformer parallelism with recurrent updates and dynamic halting to achieve Turing-completeness under assumptions and outperform standard Transformers on algorithmic and language tasks.
A Decomposable Attention Model for Natural Language Inference
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abstract
We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.
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UNVERDICTED 2representative citing papers
Framing fake news classification as natural language inference and ensembling NLI models with BERT, plus transitivity rules, achieves 88.063% test accuracy in the WSDM 2019 challenge.
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Universal Transformers
Universal Transformers combine Transformer parallelism with recurrent updates and dynamic halting to achieve Turing-completeness under assumptions and outperform standard Transformers on algorithmic and language tasks.
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Fake News Detection as Natural Language Inference
Framing fake news classification as natural language inference and ensembling NLI models with BERT, plus transitivity rules, achieves 88.063% test accuracy in the WSDM 2019 challenge.