LSTMs can learn syntax-sensitive dependencies well, but modeling structure makes them better, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp.1426-1436, 2018. ,
Linguistic generalization and compositionality in modern artificial neural networks, 2019. ,
What do neural machine translation models learn about morphology?, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp.861-872, 2017. ,
, No integration without structured representations: Response to Pater. Language, vol.95, pp.75-86, 2019.
Using deep neural networks on learn syntactic agreement. Linguistic Issues in Language Technology, vol.15, pp.1-15, 2017. ,
Deep RNNs encode soft hierarchical syntax, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp.14-19, 2018. ,
Tree-structured composition in neural networks without tree-structured architectures, NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches, 2015. ,
, Syntactic Structures. Mouton, 1957.
, Rules and representations. Behavioral and Brain Sciences, vol.3, pp.1-15, 1980.
RNN simulations of grammaticality judgments on long-distance dependencies, Proceedings of the 27th International Conference on Computational Linguistics, pp.133-144, 2018. ,
Learning auxiliary fronting with grammatical inference, Conference on Computational Language Learning, pp.125-132, 2006. ,
Unsupervised learning and grammar induction, Handbook of Computational Linguistics and Natural Language Processing, 2010. ,
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp.2126-2136, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01898412
Recurrent neural network grammars, North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016. ,
, Gottlob Frege. 1892.Über Sinn und Bedeitung. Zeitschrift für Philosophie und philosophische Kritik, vol.100, pp.25-50
Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information, EMNLP Workshop Blackbox NLP: Analyzing and Interpreting Neural Networks for NLP, pp.240-248, 2018. ,
Language identification in the limit, Information and control, vol.10, pp.447-474, 1967. ,
Colorless green recurrent networks dream hierarchically, North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.1195-1205, 2018. ,
Designing and interpreting probes with control tasks, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019. ,
A structural probe for finding syntax in word representations, Proceedings of the North American Chapter of the Association for Computational Linguistics, 2019. ,
Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure, Journal of Artificial Intelligence Research, vol.61, pp.907-926, 2018. ,
Improving text simplification language modeling using unsimplified text data, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp.1537-1546, 2013. ,
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks, 34th International Conference on Machine Learning, 2017. ,
Machine learning theory and practice as a source of insight into universal grammar, Journal of Linguistics, vol.43, pp.393-427, 2007. ,
Argument Realization, 2005. ,
Do supervised distributional methods really learn lexical inference relations?, Proceedings of the North American Chapter of the Association for Computational Linguistics Human Language Technologies, pp.970-976, 2015. ,
Specializing word embeddings (for parsing) by information bottleneck, 2019 Conference on Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing, pp.2744-2754, 2019. ,
What can linguistics and deep learning contribute to each other? Response to Pater, vol.95, pp.98-108, 2019. ,
Assessing the ability of LSTMs to learn syntax-sensitive dependencies, Transactions of the Association for Computational Linguistics, vol.4, pp.521-535, 2016. ,
Targeted syntactic evaluation of language models, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018. ,
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks, ArXiv, 2018. ,
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp.3428-3448, 2019. ,
Recurrent neural network based language model, INTER-SPEECH, 2010. ,
Grammar is grammar and usage is usage. Language, vol.79, pp.682-707, 2003. ,
Probing neural network comprehension of natural language arguments, Proceedings of the 57th Annual Meeting of the Association for Computa-tional Linguistics, pp.4658-4664, 2019. ,
Generative linguistics and neural networks at 60: Foundation, friction, and fusion, vol.95, pp.41-74, 2019. ,
Deep contextualized word representations, North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018. ,
Dissecting contextual word embeddings: Architecture and representation, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp.1499-1509, 2018. ,
Studying the inductive biases of RNNs with synthetic variations of natural languages, 2019. ,
Can LSTM learn to capture agreement? the case of basque, EMNLP Workshop Blackbox NLP: Analyzing and Interpreting Neural Networks for NLP, pp.98-107, 2018. ,
Language models learn POS first, EMNLP Workshop Blackbox NLP: Analyzing and Interpreting Neural Networks for NLP, pp.328-330, 2018. ,
Analysing mathematical reasoning abilities of neural models, Proceedings of the 7th International Conference on Learning Representations, 2019. ,
Quantity doesn't buy quality syntax with neural language models, Proceedings of Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing, pp.5830-5836, 2019. ,
A mathematical theory of communication, Bell System Technical Journal, vol.27, pp.379-423, 1948. ,
What do you learn from context? Probing for sentence structure in contextualized word representations, International Conference on Learning Representations, 2019. ,
The information bottleneck method, Annual Allerton Conference on Communication, Control and Computing, pp.368-377, 1999. ,
Feature-rich part-ofspeech tagging with a cyclic dependency network, Proceedings of the North American Chapter of the Association for Computational Linguistics, p.173180, 2003. ,