References

  1. Alfred V. Aho (1968): Indexed Grammars–An Extension of Context-Free Grammars. Journal of the ACM 15(4), pp. 647–671, doi:10.1145/321479.321488.
  2. Martin Arjovsky, Amar Shah & Yoshua Bengio (2016): Unitary Evolution Recurrent Neural Networks. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48, ICML'16. JMLR.org, pp. 1120–1128, doi:10.48550/arXiv.1511.06464.
  3. Jean-Philippe Bernardy (2018): Can RNNs Learn Nested Recursion?. Linguistic Issues in Language Technology 16, doi:10.33011/lilt.v16i.1417.
  4. Jean-Philippe Bernardy, Adam Ek & Vladislav Maraev (2021): Can the Transformer Learn Nested Recursion with Symbol Masking?. In: Findings of the ACL 2021, doi:10.18653/v1/2021.findings-acl.67.
  5. Jean-Philippe Bernardy & Shalom Lappin (2017): Using Deep Neural Networks to Learn Syntactic Agreement. Linguistic Issues In Language Technology 15(2), pp. 15, doi:10.33011/lilt.v15i.141.
  6. Jean-Philippe Bernardy & Shalom Lappin (2022): A Neural Model for Compositional Word Embeddings and Sentence Processing. In: Proceedings of The Workshop on Cognitive Modeling and Computational Linguistics. Association for Computational Linguistics, doi:10.18653/v1/2022.cmcl-1.2. Available at https://aclanthology.org/2022.cmcl-1.2/.
  7. Bob Coecke, Mehrnoosh Sadrzadeh & Stephen Clark (2010): Mathematical Foundations for a Compositional Distributional Model of Meaning. Lambek Festschrift, Linguistic Analysis 36, doi:10.48550/arXiv.1003.4394.
  8. Jeffrey L. Elman (1990): Finding structure in time. Cognitive Science 14(2), pp. 179–211, doi:10.1016/0364-0213(90)90002-E.
  9. Jeffrey L. Elman (1991): Distributed representations, simple recurrent networks, and grammatical structure. Machine learning 7(2-3), pp. 195–225, doi:10.1007/BF00114844.
  10. Edward Grefenstette, Mehrnoosh Sadrzadeh, Stephen Clark, Bob Coecke & Stephen Pulman (2011): Concrete Sentence Spaces for Compositional Distributional Models of Meaning. In: Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011). Available at https://aclanthology.org/W11-0114.
  11. Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen & Marco Baroni (2018): Colorless Green Recurrent Networks Dream Hierarchically. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, Louisiana, pp. 1195–1205, doi:10.18653/v1/N18-1108.
  12. John Hewitt, Michael Hahn, Surya Ganguli, Percy Liang & Christopher D Manning (2020): RNNs can generate bounded hierarchical languages with optimal memory. arXiv preprint arXiv:2010.07515, doi:10.18653/v1/2020.emnlp-main.156.
  13. Sepp Hochreiter & Jürgen Schmidhuber (1997): Long short-term memory. Neural Computation 9(8), pp. 1735–1780, doi:10.1162/neco.1997.9.8.1735.
  14. Stephanie L Hyland & Gunnar Rätsch (2017): Learning unitary operators with help from u(n). In: Thirty-First AAAI Conference on Artificial Intelligence, doi:10.1609/aaai.v31i1.10928.
  15. Li Jing, Yichen Shen, Tena Dubček, John Peurifoi, Scott Skirlo, Yann LeCun, Max Tegmark & Marin Soljačić (2017): Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70, ICML'17. JMLR.org, pp. 1733–1741.
  16. Aravind K. Joshi, K. Vijay Shanker & David Weir (1990): The Convergence of Mildly Context-Sensitive Grammar Formalisms. Technical Report. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA.
  17. Bobak Kiani, Randall Balestriero, Yann Lecun & Seth Lloyd (2022): projUNN: efficient method for training deep networks with unitary matrices, doi:10.48550/arXiv.2203.05483. Available at https://arxiv.org/pdf/2203.05483.pdf.
  18. Christo Kirov & Robert Frank (2012): Processing of nested and cross-serial dependencies: an automaton perspective on SRN behaviour. Connection Science 24(1), pp. 1–24, doi:10.1080/09540091.2011.641939.
  19. Joachim Lambek (2008): Pregroup Grammars and Chomsky's Earliest Examples. Journal of Logic, Language and Information 17, pp. 141–160, doi:10.1007/s10849-007-9053-2.
  20. Shalom Lappin (2021): Deep Learning and Linguistic Representation. CRC Press, Taylor & Francis, Boca Raton, London, New York, doi:10.1201/9781003127086.
  21. Tal Linzen, Emmanuel Dupoux & Yoav Golberg (2016): Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies. Transactions of the Association of Computational Linguistics 4, pp. 521–535, doi:10.1162/tacl_a_00115.
  22. Lachlan McPheat, Mehrnoosh Sadrzadeh, Hadi Wazni & Gijs Wijnholds (2021): Categorical Vector Space Semantics for Lambek Calculus with a Relevant Modality (Extended Abstract). Electronic Proceedings in Theoretical Computer Science 333, pp. 168–182, doi:10.4204/EPTCS.333.12.
  23. Stephen Pulman & G. D. Ritchie (1985): Indexed Grammars and Intersecting Dependencies. Technical Report 23. University of East Anglia.
  24. Luzi Sennhauser & Robert Berwick (2018): Evaluating the Ability of LSTMs to Learn Context-Free Grammars. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics, Brussels, Belgium, pp. 115–124, doi:10.18653/v1/W18-5414.
  25. Stuart M. Shieber (1985): Evidence against the context-freeness of natural language. Linguistics and Philosophy 8(3), pp. 333–343, doi:10.1007/BF00630917.
  26. Edward P. Stabler (2004): Varieties of crossing dependencies: Structure dependence and mild context sensitivity. Cognitive Science 93(5), pp. 699–720, doi:10.1207/s15516709cog2805_4.
  27. Mark Steedman (2000): The Syntactic Process. MIT Press, Cambridge, MA.
  28. Ilya Sutskever, James Martens & Geoffrey E. Hinton (2011): Generating Text with Recurrent Neural Networks. In: Lise Getoor & Tobias Scheffer: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. Omnipress, pp. 1017–1024. Available at https://icml.cc/2011/papers/524_icmlpaper.pdf.
  29. Gijs Wijnholds, Mehrnoosh Sadrzadeh & Stephen Clark (2020): Representation Learning for Type-Driven Composition. In: Proceedings of the 24th Conference on Computational Natural Language Learning. Association for Computational Linguistics, pp. 313–324, doi:10.18653/v1/2020.conll-1.24.
  30. Scott Wisdom, Thomas Powers, John Hershey, Jonathan Le Roux & Les Atlas (2016): Full-capacity unitary recurrent neural networks. Advances in neural information processing systems 29, pp. 4880–4888, doi:10.48550/arXiv.1611.00035.
  31. Xiang Yu, Ngoc Thang Vu & Jonas Kuhn (2019): Learning the Dyck language with attention-based Seq2Seq models. In: Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 138–146, doi:10.18653/v1/W19-4815.

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