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Differentiable Tree Operations Promote Compositional Generalization.

, , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 32499-32520. PMLR, (2023)

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Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages., , , , , , , , , and 2 other author(s). CoRR, (2022)Toward Compositional Behavior in Neural Models: A Survey of Current Views., , , , and . EMNLP, page 9323-9339. Association for Computational Linguistics, (2024)Discovering the Compositional Structure of Vector Representations with Role Learning Networks, , , and . (2019)cite arxiv:1910.09113.Differentiable Tree Operations Promote Compositional Generalization., , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 32499-32520. PMLR, (2023)Discovering the Compositional Structure of Vector Representations with Role Learning Networks., , , and . BlackboxNLP@EMNLP, page 238-254. Association for Computational Linguistics, (2020)Compositional Generalization Across Distributional Shifts with Sparse Tree Operations., , , , , and . CoRR, (2024)Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization., , , , , , , , , and . NAACL-HLT, page 4780-4793. Association for Computational Linguistics, (2021)Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels., , , and . CogSci, cognitivesciencesociety.org, (2018)