Adger, D. (2022). What are linguistic representations? Mind & Language, 37(2), 248–260.
Behme, C. (2015). Is the ontology of biolinguistics coherent? Language Sciences, 47, 32–42.
Belinkov, Y., & Glass, J. (2019). Analysis methods in neural language processing: A survey. Transactions of the Association for Computational Linguistics, 7, 49–72.
Benacerraf, P. (1973). Mathematical truth. Journal of Philosophy, 70(19), 661–679.
Blaho, S. (2007). The syntax of phonology: A radically substance-free approach (PhD Thesis). University of Tromsø.
Bloomfield, L. (1933). Language. Henry Holt.
Bloomfield, L. (1936). Language or ideas. Language, 12(2), 89–95.
Boone, W., & Piccinini, G. (2016). Mechanistic abstraction. Philosophy of Science, 83(5), 686–697.
Brentano, F. (1874/1911). Psychology from an empirical standpoint. Routledge and Kegan Paul.
Brunila, M., & LaViolette, J. (2022). What company do words keep? Revisiting the distributional semantics of J.R. Firth & Zellig Harris. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4403–4417).
Buckner, C. (2018). Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese, 195(12), 5339–5372.
Burge, T. (1986). Individualism and psychology. The Philosophical Review, 95(1), 3–45.
Cappelen, H., & Dever, J. (2021). Making AI intelligible: Philosophical foundations. Oxford University Press.
Chalmers, D. J. (1995). On implementing a computation. Minds and Machines, 4, 391–402.
Chi, E.A., Hewitt, J. & Manning, C.D. (2020). Finding universal grammatical relations in multilingual BERT. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5564–5577).
Chomsky, N. (1957). Syntactic structures. Mouton.
Chomsky, N. (1965). Aspects of the theory of syntax. MIT Press.
Chomsky, N. (1975). The logical structure of linguistic theory. Plenum press.
Chomsky, N. (1980). Rules and representations. Columbia University Press.
Chomsky, N. (1986). Knowledge of language. Praeger Publications.
Chomsky, N. (1995). The minimalist program. MIT Press.
Chomsky, N. (2012). The science of language. Cambridge University Press.
Chomsky, N., & Halle, M. (1968). The sound pattern of English. Harper & Row.
Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F. & Wattenberg, M. (2019). Visualizing and measuring the geometry of BERT. In Proceedings of the 33rd Conference on Neural Information Processing Systems (pp. 8592–8600).
Collins, J. (2014). Representations without representa: Content and illusion in linguistic theory. In P. Stalmaszczyk (Ed.), Semantics and beyond: Philosophical and linguistic inquiries (p. 2764). De Gruyter.
Collins, J. (2023). Internalist priorities in a philosophy of words. Synthese, 201(3), 110.
Collins, J., & Rey, G. (2021). Chomsky and intentionality. In N. Allott, T. Lohndal, & G. Rey (Eds.), A companion to Chomsky (pp. 488–502). Wiley.
Croft, W. A. (2001). Radical construction grammar: Syntactic theory in typological perspective. Oxford University Press.
Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B. & Sen, P. (2020). A survey of the state of explainable AI for natural language processing. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (pp. 447–459).
Dennett, D. C. (1991). Consciousness explained. Little Brown and Company.
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 4171–4186).
Dretske, F. I. (1981). Knowledge and the flow of information. The MIT Press.
Dunbar, E. (2019). Generative grammar, neural networks, and the implementational mapping problem: Response to Pater. Language, 95(1), e87–e98.
Dupre, G. (2022). Georges Rey’s representation of language. BJPS Review of Books, , Retrieved from https://www.thebsps.org/reviewofbooks/dupre-on-rey/
Egan, F. (2010). Computation models: A modest role for content. Studies in History and Philosophy of Science, 41(3), 253–259.
Egan, F. (2014). How to think about mental content. Philosophical Studies, 170(1), 115–135.
Egan, F. (2017). Function-theoretic explanation and the search for neural mechanisms. In D. Kaplan (Ed.), Explanation and integration in mind and brain science (pp. 145–163). Oxford University Press.
Egan, F. (2018). The nature and function of content in computational models. In M. Sprevak & M. Colombo (Eds.), The Routledge handbook of the computational mind (pp. 247–258). Routledge.
Facchin, M. (2022). Troubles with mathematical contents. Philosophical Psychology, 5, 1–24.
Favela, L. H., & Machery, E. (2023). Investigating the concept of representation in the neural and psychological sciences. Frontiers in Psychology, 5, 14.
Fodor, J.A. (1981). Some notes on what linguistics is about. N. Block (Ed.), Readings in philosophy of psychology, vol. II (pp. 197–207).
Fodor, J. A. (1990). A theory of content and other essays. MIT Press.
Gastaldi, J. L., & Pellissier, L. (2021). The calculus of language: Explicit representation of emergent linguistic structure through type-theoretical paradigms. Interdisciplinary Science Reviews, 46(4), 569–590.
Gleitman, L. (2021). Language as a branch of psychology: Chomsky and cognitive science. In N. Allott, T. Lohndal, & G. Rey (Eds.), A companion to Chomsky (pp. 109–122). Wiley.
Goldberg, A. E. (2006). Constructions at work: The nature of generalization in language. Oxford University Press.
Harris, Z. S. (1951). Methods in structural linguistics. The University of Chicago Press.
Haspelmath, M. (2010). Comparative concepts and descriptive categories in crosslinguistic studies. Language, 86(3), 663–687.
Haspelmath, M. (2020). Human linguisticality and the building blocks of languages. Frontiers in Psychology, 10, 3056.
Hewitt, J., & Manning, C.D. (2019). A structural probe for finding syntax in word representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4129–4138).
Immer, A., Hennigen, L.T., Fortuin, V. & Cotterell, R. (2022). Probing as quantifying inductive bias. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (pp. 1839–1851).
Iosad, P. (2017). A substance-free framework for phonology: An analysis of the Breton dialect of Bothoa. Edinburgh University Press.
Jackson, F. (1977). Perception: A representative theory. Cambridge University Press.
Jawahar, G., Sagot, B. & Seddah, D. (2019). What does BERT learn about the structure of language? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3651–3657).
Jelinek, F. (2005). Some of my best friends are linguists. Language Resources and Evaluation, 39(1), 25–34.
Kaplan, D. (2011). Explanation and description in computational neuroscience. Synthese, 183(3), 339–373.
Karlsson, F. (2006). Recursion in natural languages. In Advances in Natural Language Processing, 5th International Conference on NLP, FinTAL 2006 (p. 1).
Katz, J. (1981). Language and other abstract objects. Rowman and Littlefield.
Kovaleva, O., Romanov, A., Rogers, A. & Rumshisky, A. (2019). Revealing the dark secrets of BERT. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (pp. 4365–4374).
Kripke, S. (1980). Naming and necessity. Harvard University Press.
Kulmizev, A., & Nivre, J. (2022). Schrödinger’s tree-on syntax and neural language models. Frontiers in Artificial Intelligence, 5, 85.
Kulmizev, A., Ravishankar, V., Abdou, M. & Nivre, J. (2020). Do neural language models show preferences for syntactic formalisms? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4077–4091).
Kuokkanen, J. (2022). Vertical-horizontal distinction in resolving the abstraction, hierarchy, and generality problems of the mechanistic account of physical computation. Synthese, 200(3), 247.
Kuznetsov, I., & Gurevych, I. (2020). A matter of framing: The impact of linguistic formalism on probing results. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 171–182).
Lakoff, G. (1990). The invariance hypothesis: Is abstract reason based on imageschemas? Cognitive Linguistics, 1(1), 39–74.
Langacker, R. W. (1987). Foundations of cognitive grammar, volume 1, theoretical prerequisites. Stanford University Press.
Lasri, K., Pimentel, T., Lenci, A., Poibeau, T. & Cotterell, R. (2022). Probing for the usage of grammatical number. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers (pp. 8818–8831).
Laurence, S. (2003). Is linguistics a branch of psychology? In A. Barber (Ed.), Epistemology of language (pp. 69–106). Oxford University Press.
Levine, R. (2018). ‘Biolinguistics’: Some foundational problems. In C. Behme & M. Neef (Eds.), Essays on linguistic realism (pp. 21–60). John Benjamins Publishing Company.
Levy, A. (2013). Three kinds of new mechanism. Biology and Philosophy, 28(1), 99–114.
Lewis, D. (1970). How to define theoretical terms. Journal of Philosophy, 67(13), 426–446.
Li, J., Cotterell, R. & Sachan, M. (2022). Probing via prompting. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1144–1157).
Li, L., Ma, R., Guo, Q., Xue, X. & Qiu, X. (2020). BERT-ATTACK: Adversarial attack against BERT using BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 6193–6202).
Linzen, T., & Baroni, M. (2021). Syntactic structure from deep learning. Annual Review of Linguistics, 7, 195–212.
Madabushi, H.T., Romain, L., Divjak, D. & Milin, P. (2020). CXGBERT: BERT meets construction grammar. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 4020–4032).
Manning, C. D., Clark, K., & Hewitt, J. (2020). Emergent linguistic structure in artificial neural networks trained by self-supervision. PNAS, 117(48), 30046–30054.
Marcus, G. F. (1998). Rethinking eliminative connectionism. Cognitive Psychology, 37(3), 243–282.
Marr, D. (1982). Vision. W.H. Freeman and Company.
Matthews, R. J. (2007). The measure of mind: Propositional attitudes and their attribution. Oxford University Press.
McCoy, T., Frank, R., & Linzen, T. (2020). Does syntax need to grow on trees? Sources of hierarchical inductive bias in sequence-to-sequence networks. Transactions of the Association for Computational Linguistics, 8, 125–140.
McCoy, T., Pavlick, E. & Linzen, T. (2019). Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3428–3448).
Mickus, T., Paperno, D., Constant, M. & van Deemter, K. (2020). What do you mean, BERT? Assessing BERT as a distributional semantics model. In Proceedings of the Society for Computation in Linguistics (pp. 350–361).
Miller, P. H. (1999). Strong generative capacity: The semantics of linguistic formalism. CSLI Publications.
Millikan, R. G. (1993). Content and vehicle. In N. Eilan, R. McCarthy, & B. Brewer (Eds.), Spatial representation (pp. 256–268). Blackwell.
Millikan, R. G. (2017). Beyond concepts: Unicepts, language, and natural information. Oxford University Press.
Mueller, A., Frank, R., Linzen, T., Wang, L. & Schuster, S. (2022). Coloring the blank slate: Pre-training imparts a hierarchical inductive bias to sequence-to-sequence models. In Findings of the Association for Computational Linguistics: ACL 2022 (pp. 1352–1368).
Nadeem, M., Bethke, A. & Reddy, S. (2020). StereoSet: Measuring stereotypical bias in pretrained 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 (pp. 5356–5371).
Neander, K. (2017). A mark of the mental: A defence of informational teleosemantics. MIT Press.
Nefdt, R. M. (2023). Language, science, and structure: A journey into the philosophy of linguistics. Oxford University Press.
Newmeyer, F. (2010). On comparative concepts and descriptive categories: A reply to Haspelmath. Language, 86(3), 688–695.
Odden, D. (2013). Formal phonology. Nordlyd, 40(1), 249–273.
OpenAI (2023). GPT-4 technical report (Tech. Rep.).
Ott, D. (2017). Strong generative capacity and the empirical base of linguistic theory. Frontiers in Psychology, 7, 8.
Pater, J. (2019). Generative linguistics and neural networks at 60: Foundation, friction, and fusion. Language, 95(1), e41–e74.
Pennington, J., Socher, R. & Manning, C.D. (2014). GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1532–1543).
Piccinini, G. (2015). Physical computation: A mechanistic account. Oxford University Press.
Pinker, S., & Price, A. (1988). On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition, 28(1–2), 73–193.
Poeppel, D., & Embick, D. (2005). Defining the relation between linguistics and neuroscience. In A. Cutler (Ed.), Twenty-first century psycholinguistics: Four cornerstones (pp. 1–16). Lawrence and Erlbaum Associates.
Postal, P. (2003). Remarks on the foundations of linguistics. The Philosophical Forum, 34(3–4), 233–252.
Postal, P. (2009). The incoherence of Chomsky’s ‘biolinguistic’ ontology. Biolinguistics, 3(1), 104–123.
Putnam, H. (1988). Representation and reality. MIT Press.
Quine, W. V. O. (1970). Methodological reflections on current linguistic theory. Synthese, 21, 386–398.
Rey, G. (2020). Representation of language: Philosophical issues in a Chomskyan linguistics. Oxford University Press.
Rogers, A., Kovaleva, O., & Rumshisky, A. (2020). A primer in BERTology: What we know about how BERT works. Transactions of the Association for Computational Linguistics, 8, 842–866.
Rumelhart, D. E., & McClelland, J. L. (1986). On learning the past tenses of English verbs. In J. L. McClelland, D. E. Rumelhart, & T. P. R. Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 2. psychological and biological models (pp. 216–271). MIT Press.
Sennrich, R., Haddow, B. & Birch, A. (2016). Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1715–1725).
Smith, B. C. (2006). Why we still need knowledge of language. Croatian Journal of Philosophy, 6(3), 431–456.
Soler, A.G., & Apidianaki, M. (2020). BERT knows Punta Cana is not just beautiful, it’s gorgeous: Ranking scalar adjectives with contextualized representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 7371–7385).
Sprevak, M. (2018). Triviality arguments about computational implementation. In M. Sprevak & M. Colombo (Eds.), Routledge handbook of the computational mind (pp. 175–191). Routledge.
Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87(3), 449–508.
Tenney, I., Das, D. & Pavlick, E. (2019). BERT rediscovers the classical NLP pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4593–4601).
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Polosukhins, I. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing (pp. 6000–6010).
Weiss, G., Goldberg, Y. & Yahav, E. (2021). Thinking like transformers. In Proceedings of the 38th international conference on machine learning (pp. 11080–11090).
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