From language to language-ish: How brain-like is an LSTM
representation of nonsensical language stimuli?
Hashemzadeh, M., Kaufeld, G., White, M., Martin, A. E., & Fyshe, A.
From language to language-ish: How brain-like is an LSTM representation of nonsensical language stimuli? In T. Cohn, Y. He, & Y. Liu (Eds.
), Findings of the Association for Computational Linguistics: EMNLP 2020
(pp. 645-655). Association for Computational Linguistics.
The representations generated by many mod-
els of language (word embeddings, recurrent
neural networks and transformers) correlate
to brain activity recorded while people read.
However, these decoding results are usually
based on the brain’s reaction to syntactically
and semantically sound language stimuli. In
this study, we asked: how does an LSTM (long
short term memory) language model, trained
(by and large) on semantically and syntac-
tically intact language, represent a language
sample with degraded semantic or syntactic
information? Does the LSTM representation
still resemble the brain’s reaction? We found
that, even for some kinds of nonsensical lan-
guage, there is a statistically signiﬁcant rela-
tionship between the brain’s activity and the
representations of an LSTM. This indicates
that, at least in some instances, LSTMs and the
human brain handle nonsensical data similarly.