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Belinda Z. Li Maxwell Nye Jacob Andreas Massachusetts Institute of Technology fbzl,mnye,[email protected] Abstract Does the effectiveness of neural language mod-els derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the
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In BART and T5 transformer language models, we identify contextual word representations that function as models of entities and situations as they evolve throughout a discourse. These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity's current properties.
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Cite (ACL): Belinda Z. Li, Maxwell Nye, and Jacob Andreas. 2021. Implicit Representations of Meaning in Neural Language Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1813-1827, Online.
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Other names Belinda Li. MIT. Verified email at mit.edu - Homepage. Natural Language Processing Machine Learning AI. Articles Cited by Co-authors. Title. Sort. Sort by citations Sort by year Sort by title. Cited by. Cited by. Year; Linformer: Self-Attention with Linear Complexity. S Wang, B Li, M Khabsa, H Fang, H Ma.
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Li, Belinda Z, Nye, Maxwell and Andreas, Jacob. 2021. "Implicit Representations of Meaning in Neural Language Models." Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Version: Final published version.
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