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Communicating artificial neural networks develop efficient color-naming systems
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In: ISSN: 0027-8424 ; EISSN: 1091-6490 ; Proceedings of the National Academy of Sciences of the United States of America ; https://hal.inria.fr/hal-03329084 ; Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2021, 118 (12), ⟨10.1073/pnas.2016569118⟩ (2021)
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On the proper role of linguistically-oriented deep net analysis in linguistic theorizing ...
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Communicating artificial neural networks develop efficient color-naming systems
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In: Proc Natl Acad Sci U S A (2021)
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Compositionality and Generalization in Emergent Languages
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In: ACL 2020 - 8th annual meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02959466 ; ACL 2020 - 8th annual meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States (2020)
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Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans ...
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A Benchmark for Systematic Generalization in Grounded Language Understanding ...
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Linguistic generalization and compositionality in modern artificial neural networks
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In: Philos Trans R Soc Lond B Biol Sci (2020)
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Anti-efficient encoding in emergent communication
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In: https://hal.archives-ouvertes.fr/hal-02274205 ; 2019 (2019)
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Word-order biases in deep-agent emergent communication
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In: ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02274157 ; ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Jul 2019, Florence, Italy (2019)
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EGG: a toolkit for research on Emergence of lanGuage in Games
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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations ; https://hal.archives-ouvertes.fr/hal-02274229 ; Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, Nov 2019, Hong Kong, China. ⟨10.18653/v1/D19-3010⟩ (2019)
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On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study ...
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Focus on What's Informative and Ignore What's not: Communication Strategies in a Referential Game ...
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Tabula nearly rasa: Probing the Linguistic Knowledge of Character-Level Neural Language Models Trained on Unsegmented Text ...
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Colorless green recurrent networks dream hierarchically
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In: Proceedings of the Society for Computation in Linguistics (2019)
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What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
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In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01898412 ; ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, Australia. pp.2126-2136 (2018)
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Abstract:
International audience ; Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. "Downstream" tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.archives-ouvertes.fr/hal-01898412
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How agents see things: On visual representations in an emergent language game ...
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What you can cram into a single vector: Probing sentence embeddings for linguistic properties ...
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Colorless green recurrent networks dream hierarchically ...
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