17 research outputs found
Language comprehension as a multi-label classification problem
The initial stage of language comprehension is a multi-label
classification problem. Listeners or readers, presented with
an utterance, need to discriminate between the intended
words and the tens of thousands of other words they know.
We propose to address this problem by pairing a network
trained with the learning rule of Rescorla andWagner (1972)
with a second network trained independently with the learning
rule of Widrow and Hoff (1960). The first network has
to recover from sublexical input features the meanings encoded
in the language signal, resulting in a vector of activations
over the lexicon. The second network takes this
vector as input and further reduces uncertainty about the
intended message. Classification performance for a lexicon
with 52,000 entries is good. The model also correctly predicts
several aspects of human language comprehension. By
rejecting the traditional linguistic assumption that language
is a (de)compositional system, and by instead espousing a
discriminative approach (Ramscar, 2013), a more parsimonious
yet highly effective functional characterization of the
initial stage of language comprehension is obtained