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20182020
most citedNeural Language Models as Psycholinguistic Subjects: Representations of Syntactic State

12 citations · 12 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CL2020

Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)

Takashi Morita, Hiroki Koda

In this study, we reported our exploration of Text-To-Speech without Text (TTS without T) in the Zero Resource Speech Challenge 2020, in which participants proposed an end-to-end,…

cs.CL201912 cited

Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State

Richard Futrell, Ethan Wilcox, Takashi Morita +3

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 repres…

q-bio.NC2018

Superregular grammars do not provide additional explanatory power but allow for a compact analysis of animal song

Takashi Morita, Hiroki Koda

A pervasive belief with regard to the differences between human language and animal vocal sequences (song) is that they belong to different classes of computational complexity, wit…

cs.CL2018

RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency

Richard Futrell, Ethan Wilcox, Takashi Morita +1

Recurrent neural networks (RNNs) are the state of the art in sequence modeling for natural language. However, it remains poorly understood what grammatical characteristics of natur…

cs.CL2018

What do RNN Language Models Learn about Filler-Gap Dependencies?

Ethan Wilcox, Roger Levy, Takashi Morita +1

RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they l…