The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Emergent linguistic structure in artificial neural networks trained by self-supervision
Open accessAbstract
This paper explores the knowledge of linguistic structure learned by large artificial neural networks, trained via self-supervision, whereby the model simply tries to predict a masked word in a given context. Human language communication is via sequences of words, but language understanding requires constructing rich hierarchical structures that are never observed explicitly. The mechanisms for this have been a prime mystery of human language acquisition, while engineering work has mainly proceeded by supervised learning on treebanks of sentences hand labeled for this latent structure. However, we demonstrate that modern deep contextual language models learn major aspects of this structure, without any explicit supervision. We develop methods for identifying linguistic hierarchical structure emergent in artificial neural networks and demonstrate that components in these models focus on syntactic grammatical relationships and anaphoric coreference. Indeed, we show that a linear transformation of learned embeddings in these models captures parse tree distances to a surprising degree, allowing approximate reconstruction of the sentence tree structures normally assumed by linguists. These results help explain why these models have brought such large improvements across many language-understanding tasks.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Self-citationDistributed Representations of Words and Phrases and their Compositionality
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Advances in Neural Information Processing Systems 19
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Advances in Neural Information Processing Systems 29
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Deep Contextualized Word Representations
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Self-citationA hierarchy of linguistic predictions during natural language comprehension
Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P., & de Lange, F. P. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201968119 · Cited by 343
More than a Feeling: Accuracy and Application of Sentiment Analysis
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Metadata
- Authors
- Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., & Levy, O.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2020-06-03
- Volume
- 117
- Issue
- 48
- Pages
- 30046-30054
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W3031914912
- Cited by
- 339
- References
- 63
- FWCI
- 18.37
- Type
- journal article
- Language
- en
- Open access
- green
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