Fetching the paper…
Reading the bibliography…
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language.
Multi-space variational encoder-decoders for semi-supervised labeled sequence transduction
Zhou, C. and G. Neubig (2017) · 1904
Earlier work this paper cites.
Context-free grammars and pushdown storage
Chomsky, N. (1962) · 1962
Earlier work this paper cites.
Constraints on Variables in Syntax
Ross, J. R. (1967) · 1967
Earlier work this paper cites.
Finding structure in time
Elman, J. L. (1990) · 1990
Earlier work this paper cites.
On the computational power of neural nets
Siegelmann, H. T. and E. D. Sontag (1995) · 1995
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and J. Schmidhuber (1997) · 1997
Earlier work this paper cites.
Review and comparison of methods to study the contribution of variables in artificial neural network models
Gevrey, M., I. Dimopoulos, and S. Lek (2003) · 2003
Earlier work this paper cites.
Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
Hendrickx, I., S. N. Kim, Z. Kozareva, P. Nakov, D. Ó Séaghdha, S. Padó, M. Pennacchiotti, L. Romano, and S. Szpakowicz (2009) · 2010
Earlier work this paper cites.
Negative and positive polarity items: Variation, licensing, and compositionality
Giannakidou, A. (2011) · 2011
Earlier work this paper cites.
Density-based clustering based on hierarchical density estimates
Campello, R. J., D. Moulavi, and J. Sander (2013) · 2013
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek (2015) · 2015
Earlier work this paper cites.
Learning language through pictures
Chrupała, G., À. Kádár, and A. Alishahi (2015) · 2015
Earlier work this paper cites.
Learning to transduce with unbounded memory
Grefenstette, E., K. M. Hermann, M. Suleyman, and P. Blunsom (2015) · 2015
Earlier work this paper cites.
Skip-thought vectors
Kiros, R., Y. Zhu, R. R. Salakhutdinov, R. Zemel, R. Urtasun, A. Torralba, and S. Fidler (2015) · 2015
Earlier work this paper cites.
Assessing the ability of lstms to learn syntax-sensitive dependencies
Linzen, T., E. Dupoux, and Y. Goldberg (2016) · 2016
Earlier work this paper cites.
Combining recurrent and convolutional neural networks for relation classification
Vu, N. T., H. Adel, P. Gupta, et al. (2016) · 2016
Earlier work this paper cites.
Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Adi, Y., E. Kermany, Y. Belinkov, O. Lavi, and Y. Goldberg (2017) · 2017
Earlier work this paper cites.
Encoding of phonology in a recurrent neural model of grounded speech
Alishahi, A., M. Barking, and G. Chrupała (2017) · 2017
Earlier work this paper cites.
Deep learning in semantic kernel spaces
Croce, D., S. Filice, G. Castellucci, and R. Basili (2017) · 2017
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Jang, E., S. Gu, and B. Poole (2017) · 2017
Earlier work this paper cites.
Representation of linguistic form and function in recurrent neural networks
Kádár, A., G. Chrupała, and A. Alishahi (2017) · 2017
Earlier work this paper cites.
Learning how to explain neural networks: Patternnet and patternattribution
Kindermans, P.-J., K. T. Schütt, M. Alber, K.-R. Müller, D. Erhan, B. Kim, and S. Dähne (2017) · 2017
Earlier work this paper cites.
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller (2017) · 2017
Earlier work this paper cites.
A regularized framework for sparse and structured neural attention
Niculae, V. and M. Blondel (2017) · 2017
Earlier work this paper cites.
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Raghu, M., J. Gilmer, J. Yosinski, and J. Sohl-Dickstein (2017) · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin (2017) · 2017
Earlier work this paper cites.
Probing sentence embeddings for structure-dependent tense
Bacon, G. and T. Regier (2018) · 2018
Cited alongside, same era.
Jump to better conclusions: Scan both left and right
Bastings, J., M. Baroni, J. Weston, K. Cho, and D. Kiela (2018) · 2018
Cited alongside, same era.
Exploiting attention to reveal shortcomings in memory models
Burns, K., A. Nematzadeh, E. Grant, A. Gopnik, and T. Griffiths (2018) · 2018
Cited alongside, same era.
On the role of text preprocessing in neural network architectures: An evaluation study on text categorization and sentiment analysis
Camacho-Collados, J. and M. T. Pilehvar (2018) · 2018
Cited alongside, same era.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Conneau, A., G. Kruszewski, G. Lample, L. Barrault, and M. Baroni (2018) · 2018
Cited alongside, same era.
Explaining non-linear classifier decisions within kernel-based deep architectures
Interpretable structure induction via sparse attention
Peters, B., V. Niculae, and A. F. T. Martins (2018) · 2018
Later among the works it cites.
Interpretable textual neuron representations for nlp
Poerner, N., B. Roth, and H. Schütze (2018) · 2018
Later among the works it cites.
Collecting diverse natural language inference problems for sentence representation evaluation
Poliak, A., A. Haldar, R. Rudinger, J. E. Hu, E. Pavlick, A. S. White, and B. Van Durme (2018) · 2018
Later among the works it cites.
An analysis of encoder representations in transformer-based machine translation
Raganato, A. and J. Tiedemann (2018) · 2018
Later among the works it cites.
Can lstm learn to capture agreement? the case of basque
Ravfogel, S., Y. Goldberg, and F. Tyers (2018) · 2018
Later among the works it cites.
Linguistic representations in multi-task neural networks for ellipsis resolution
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Croce, D., D. Rossini, and R. Basili (2018) · 2018
Cited alongside, same era.
Does syntactic knowledge in multilingual language models transfer across languages?
Dhar, P. and A. Bisazza (2018) · 2018
Cited alongside, same era.
Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information
Giulianelli, M., J. Harding, F. Mohnert, D. Hupkes, and W. Zuidema (2018) · 2018
Cited alongside, same era.
Colorless green recurrent networks dream hierarchically
Gulordava, K., P. Bojanowski, E. Grave, T. Linzen, and M. Baroni (2018) · 2018
Cited alongside, same era.
Lisa: Explaining recurrent neural network judgments via layer-wise semantic accumulation and example to pattern transformation
Gupta, P. and H. Schütze (2018) · 2018
Cited alongside, same era.
Context-free transductions with neural stacks
Hao, Y., W. Merrill, D. Angluin, R. Frank, N. Amsel, A. Benz, and S. Mendelsohn (2018) · 2018
Cited alongside, same era.
Learning explanations from language data
Harbecke, D., R. Schwarzenberg, and C. Alt (2018) · 2018
Cited alongside, same era.
Rønning, O., D. Hardt, and A. Søgaard (2018) · 2018
Later among the works it cites.
Evaluating the ability of lstms to learn context-free grammars
Sennhauser, L. and R. Berwick (2018) · 2018
Later among the works it cites.
Neural language modeling by jointly learning syntax and lexicon
Shen, Y., Z. Lin, C.-W. Huang, and A. Courville (2018) · 2018
Later among the works it cites.
Closing brackets with recurrent neural networks
Skachkova, N., T. Trost, and D. Klakow (2018) · 2018
Later among the works it cites.
Nightmare at test time: How punctuation prevents parsers from generalizing
Søgaard, A., M. de Lhoneux, and I. Augenstein (2018) · 2018
Later among the works it cites.
Firearms and tigers are dangerous, kitchen knives and zebras are not: Testing whether word embeddings can tell
Sommerauer, P. and A. Fokkens (2018) · 2018
Later among the works it cites.
Evaluating textual representations through image generation
Spinks, G. and M.-F. Moens (2018) · 2018
Later among the works it cites.
An operation sequence model for explainable neural machine translation
Stahlberg, F., D. Saunders, and B. Byrne (2018) · 2018
Later among the works it cites.
Rule induction for global explanation of trained models
Sushil, M., S. Suster, and W. Daelemans (2018) · 2018
Later among the works it cites.
Learning and evaluating sparse interpretable sentence embeddings
Trifonov, V., O.-E. Ganea, A. Potapenko, and T. Hofmann (2018) · 2018
Later among the works it cites.
Iterative recursive attention model for interpretable sequence classification
Tutek, M. and J. Šnajder (2018) · 2018
Later among the works it cites.
Explicitly modeling case improves neural dependency parsing
Vania, C. and A. Lopez (2018) · 2018
Later among the works it cites.
State gradients for rnn memory analysis
Verwimp, L., H. Van Hamme, V. Renkens, and P. Wambacq (2018b, Sep) · 2018
Later among the works it cites.
Interpreting neural networks with nearest neighbors
Wallace, E., S. Feng, and J. Boyd-Graber (2018) · 2018
Later among the works it cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman (2018) · 2018
Later among the works it cites.
Evaluating grammaticality in seq2seq models with a broad coverage hpsg grammar: A case study on machine translation
Wei, J., K. Pham, B. O’Connor, and B. Dillon (2018) · 2018
Later among the works it cites.
On the practical computational power of finite precision rnns for language recognition
Weiss, G., Y. Goldberg, and E. Yahav (2018) · 2018
Later among the works it cites.
What do rnn language models learn about filler–gap dependencies?
Wilcox, E., R. Levy, T. Morita, and R. Futrell (2018) · 2018
Later among the works it cites.
Do latent tree learning models identify meaningful structure in sentences?
Williams, A., A. Drozdov, and S. R. Bowman (2018) · 2018
Later among the works it cites.
StructVAE: Tree-structured latent variable models for semi-supervised semantic parsing
Yin, P., C. Zhou, J. He, and G. Neubig (2018) · 2018
Later among the works it cites.
What can linguistics and deep learning contribute to each other? response to pater
Linzen, T. (2019) · 2019
Closest in time.