Multilingual Distributional Semantic Models: Toward a Computational Model of the Bilingual Mental Lexicon

In this paper, we propose a novel framework of a multilingual distributional semantic model to provide a psychologically plausible computational model of the bilingual mental lexicon. In the proposed framework, a monolingual semantic space for each target language is first generated from the corresponding monolingual corpus. These monolingual semantic spaces are then converted into ones with common dimensions, which are in turn integrated into a single multilingual semantic space. The language of dimensions, which we refer to as a pivot language, determines the type of bilinguals simulated by the model. We also tested the psychological plausibility of the proposed multilingual distributional semantic model by comparing the cosine similarity computed by the model with the cross-language word similarity ratings of L1 Japanese/L2 English sequential bilinguals. The result was that the bilingual semantic space with Japanese as a pivot language, which is predicted to be a model for L1 Japanese/L2 English sequential bilinguals, achieved better performance in simulating the similarity rating data. This suggests the plausibility of the proposed multilingual model.