Toward Computational Models of Multilingual Sentence Processing

Stefan L Frank

doi:10.1111/lang.12406

Abstract

AbstractAlthough computational models can simulate aspects of human sentence processing, research on this topic has remained almost exclusively limited to the single language case. The current review presents an overview of the state of the art in computational cognitive models of sentence processing, and discusses how recent sentence‐processing models can be used to study bi‐ and multilingualism. Recent results from cognitive modeling and computational linguistics suggest that phenomena specific to bilingualism can emerge from systems that have no dedicated components for handling multiple languages. Hence, accounting for human bi‐/multilingualism may not require models that are much more sophisticated than those for the monolingual case.

Highlights

Computational modeling has been fundamental to the cognitive sciences and continues to be one of the most valuable methods for studying human cog
Performance of the bilingual model in either language was similar to performance of each of two monolingual networks. This result is impressive because the English, French, and bilingual recurrent neural network (RNN) models did not differ in their input and recurrent connection weights; only the weights going to the output units were trained on sentences from one or both languages
What is the potential for cognitive models of multilingualism? The results on bilingual RNNs suggest that multilingualism is relatively straightforward to model: Next-word prediction and sentence-production RNNs can, in principle, be trained on one, two, or many languages

Summary

Introduction

Computational modeling has been fundamental to the cognitive sciences and continues to be one of the most valuable methods for studying human cog-I am thankful to Sol Lago, Xavier Hinaut, Jan Vanhove, Raphael Berthele, and three anonymous reviewers for their comments on earlier versions of this paper. Keywords multilingualism; sentence processing; computational models; probabilistic grammars; neural networks

Results

Conclusion