Transformer Working Memory Enables Regular Language Reasoning And Natural Language Length Extrapolation - researchr publication authors

researchr

You are not signed in
Sign in
Sign up

Ta-Chung Chi, Ting-Han Fan, Alexander Rudnicky, Peter J. Ramadge. Transformer Working Memory Enables Regular Language Reasoning And Natural Language Length Extrapolation. In Houda Bouamor, Juan Pino 0001, Kalika Bali, editors, Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023. pages 5972-5984, Association for Computational Linguistics, 2023. [doi]

This author has not been identified. Look up 'Ta-Chung Chi' in GoogleThis author has not been identified. Look up 'Ting-Han Fan' in GoogleThis author has not been identified. Look up 'Alexander Rudnicky' in GoogleThis author has not been identified. Look up 'Peter J. Ramadge' in Google

runs on WebDSL