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We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental representations of syntactic state.
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Adrian Staub and Charles Clifton. 2006 · 2006
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The parser doesn’t ignore intransitivity, after all
Adrian Staub. 2007 · 2007
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Mixed-effects modeling with crossed random effects for subjects and items
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Modeling the effects of memory on human online sentence processing with particle filters
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Stefan L. Frank and Rens Bod. 2011 · 2011
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Exploring the syntactic abilities of RNNs with multi-task learning
Emile Enguehard, Yoav Goldberg, and Tal Linzen. 2017 · 2017
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Neural network methods for natural language processing
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Grammaticality, acceptability, and probability: a probabilistic view of linguistic knowledge
Jey Han Lau, Alexander Clark, and Shalom Lappin. 2017 · 2017
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Effective inference for generative neural parsing
Mitchell Stern, Daniel Fried, and Dan Klein. 2017 · 2017
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RNN simulations of grammaticality judgments on long-distance dependencies
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Bert: Pre-training of deep bidirectional transformers for language understanding
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The processing of extraposed structures in English
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Random effects structure for confirmatory hypothesis testing: Keep it maximal
Dale J Barr, Roger Levy, Christoph Scheepers, and Harry J Tily. 2013 · 2013
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One billion word benchmark for measuring progress in statistical language modeling
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The effect of word predictability on reading time is logarithmic
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Recurrent neural network grammars
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Predictive power of word surprisal for reading times is a linear function of language model quality
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Colorless green recurrent networks dream hierarchically
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Finding syntax in human encephalography with beam search
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LSTMs can learn syntax-sensitive dependencies well, but modeling structure makes them better
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Targeted syntactic evaluation of language models
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Deep contextualized word representations
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A neural model of adaptation in reading
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What do rnn language models learn about filler–gap dependencies?
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