Neurophysiological dynamics of phrase-structure building during sentence processing

By M. Nelson, Imen El Karoui, K. Giber, Xiaofang Yang, Leonardo G. Cohen, H. Koopman, Sydney S. Cash, Lionel Naccache, J. Hale, Christophe Pallier and 1 more. 2017

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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (2018, background): “…an un-trained human subject to be explicitly aware of tree depth or top constituency, similar information must be implicitly computed to correctly parse sentences, and there is suggestive evidence that the brain tracks something akin to tree depth during sentence processing (Nelson et al., 2017).”
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better (2018, background): “…processing, especially in terms of memory constraints and human difficulties with center embedding constructions (Johnson-Laird, 1983; Pulman, 1986; Abney and Johnson, 1991; Resnik, 1992, inter alia), along with neurophysiological evidence in human sentence processing (Nelson et al., 2017).”
The emergence of number and syntax units in LSTM language models (2019, background): “Other studies are closer to our approach in that they attempt to attribute function to specific network cells, often by means of visualization (Karpathy et al., 2016; Li et al., 2016; Tang et al., 2017).”
Discovering event structure in continuous narrative perception and memory (2016, unknown): “, phonemes, words, sentences, paragraphs) before providing its output to the next processing level (Nelson et al., 2017).”