Key papers

Abnar, S., Beinborn, L., Choenni, R., & Zuidema, W. (2019). Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains.

Beeksma, M., Verberne, S., van den Bosch, A., Das, E., Hendrickx, I., & Groenewoud, S. (2019). Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records. BMC Medical Informatics and Decision Making, 19, 36.

Chrupała, G., & Alishahi, A. (2019). Correlating neural and symbolic representations of language. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

Mul, M., & Zuidema, W. (2019). Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization.

Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2018). Neural versus phrase-based MT quality: An in-depth analysis on English–German and English–French. Computer Speech & Language, 49, 52–70.

Bisazza, A., & Tump, C. (2018). The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 2871–2876). Brussels, Belgium: Association for Computational Linguistics.

Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. (2018). Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. In Proceedings EMNLP workshop Analyzing and interpreting neural networks for NLP (BlackboxNLP).

Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and ‘Diagnostic Classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61, 907–926.

Murdoch, W. J., Liu, P. J., & Yu, B. (2018). Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. ICLR 2018.

Repplinger, M., Beinborn, L., & Zuidema, W. (2018). Vector-space models of words and sentences. Nieuw Archief Voor De Wiskunde.

Tran, K., Bisazza, A., & Monz, C. (2018). The Importance of Being Recurrent for Modeling Hierarchical Structure. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 4731–4736).

Alishahi, A., Barking, M., & Chrupała, G. (2017). Encoding of phonology in a recurrent neural model of grounded speech. In R. Levy & L. Specia (Eds.), Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) (pp. 368–378). Association for Computational Linguistics.

Arras, L., Montavon, G., Müller, K.-R., & Samek, W. (2017). Explaining Recurrent Neural Network Predictions in Sentiment Analysis. EMNLP 2017, 159.

Fadaee, M., Bisazza, A., & Monz, C. (2017). Data Augmentation for Low-Resource Neural Machine Translation. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 567–573.

Fokkens, A., ter Braake, S., Ockeloen, N., Vossen, P., Legêne, S., Schreiber, G., & de Boer, V. (2017). BiographyNet: Extracting Relations Between People and Events. In Á. Z. Bernád, C. Gruber, & M. Kaiser (Eds.), Europa baut auf Biographien: Aspekte, Bausteine, Normen und Standards für eine europäische Biographik (1st ed., pp. 193–224). Vienna: New Academic Press.

Kádár, Á., Chrupała, G., & Alishahi, A. (2017). Representation of Linguistic Form and Function in Recurrent Neural Networks. Computational Linguistics, 43, 761–780.

Le, M., & Fokkens, A. (2017). Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing. ArXiv:1702.06794 [Cs].

Raaijmakers, S., Sappelli, M., & Kraaij, W. (2017). Investigating the Interpretability of Hidden Layers in Deep Text Mining. In Proceedings of the 13th International Conference on Semantic Systems (pp. 177–180). New York, NY, USA: ACM.

Veldhoen, S., & Zuidema, W. (2017). Can Neural Networks learn Logical Reasoning? In Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML) (pp. pp. 35–41). University of Gothenburgh, Sweden.

Tran, K., Bisazza, A., & Monz, C. (2016). Recurrent Memory Networks for Language Modeling. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 321–331).

Veldhoen, S., Hupkes, D., & Zuidema, W. (2016). Diagnostic classifiers: revealing how neural networks process hierarchical structure. In Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (at NIPS).

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS ONE, 10, e0130140.

Le, P., & Zuidema, W. (2015). Compositional Distributional Semantics with Long Short Term Memory. In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics (pp. 10–19). Denver, Colorado: Association for Computational Linguistics.

Lentz, T. O., & Chen, A. (2015). Unbalanced adult production and perception in prosody. In Proceedings of the 18th International Congress of Phonetic Sciences. University of Glasgow, Glasgow.

Tran, K. M., Bisazza, A., & Monz, C. (2014). Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1676–1688). Association for Computational Linguistics.

Bisazza, A., & Federico, M. (2012). Cutting the Long Tail: Hybrid Language Models for Translation Style Adaptation. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (pp. 439–448). Avignon, France: Association for Computational Linguistics.