RNN Simulations of Grammaticality Judgments on Long-distance Dependencies
Shammur Absar Chowdhury, Roberto Zamparelli · Iris (University of Trento) · 2018
The paper explores the ability of LSTM networks trained on a language modeling task to detect linguistic structures which are ungrammatical due to extraction violations (extra arguments and subject-relative clause island violations), and considers its implications for the debate on language innatism.The results show that the current RNN model can correctly classify (un)grammatical sentences, in certain conditions, but it is sensitive to linguistic processing factors and probably ultimately unable to induce a more abstract notion of grammaticality, at least in the domain we tested.