Lexicon and Syntax in a Bilingual Connectionist Network - eScholarship

Lexicon and Syntax in a Bilingual Connectionist Network Valderes Aparecida Rinaldi (valderesrinaldi@yahoo.com.br) Department of Linguistics, State University of Campinas P.O. Box 6045, 13083-970 Campinas SP BRAZIL. Ricardo Basso Garcia (rbgarcia@gmail.com) Department of Linguistics, State University of Campinas P.O. Box 6045, 13083-970 Campinas SP BRAZIL Edson Francozo (edson@unicamp.br) Department of Linguistics, State University of Campinas P.O. Box 6045, 13083-970 Campinas SP BRAZIL. Abstract If bilingualism is a common phenomenon, research on bilingualism using recurrent networks is not. Few models using recurrent neural networks were developed in the past years in an attempt to address, among others, the question: is the lexical knowledge about two (or more) languages kept apart? French’s (1998) results have shown that there are two separate regions in the lexicon, each one corresponding to one specific language. The present study extends French’s model by including syntactic differences between the languages in addition to the exclusively lexical ones in the original simulation. Our network not only kept the lexicons apart, but also represented more fine grained structural differences between the languages in the hidden layer activation space. Keywords: acquisition. bilingualism; connectionism; language Introduction Studies in bilingual knowledge and learning have grown into a full blown research field during the past twenty years (or so). In the past few years, experimental and sophisticated tools have allowed for considerable advances in the area, making room for the investigation of central questions about memory and language processing in bilinguals. Computational modeling is also part of this landscape (for reviews, see French & Jacquet, 2004, and Thomas & van Heuven, 2005) and connectionist models are plenty: BIA (Bilingual Interactive Activation; Dijkstra & van Heuven, 1998), BIMOLA (Bilingual Model of Lexical Access; Grosjean, 1997), SOMBIP (Self-Organizing Connectionist Model of Bilingual Processing; Li & Farkas, 2002), Hasta La Vista, Baby (Scutt, 1997), BSRN (Bilingual Simple Recurrent Network; French, 1998) and BLLCN (Bilingual Language Learning in Connectionist Networks; Liang, 2004). Among these, Simple Recurrent Networks (SRNs) simulations allow for important aspects of the problem to be explored – on the one hand, the bilingual acquisition of language and, on the other, the dynamics of the mechanisms responsible for bilingual knowledge organization. French (1998) put forward a simulation in which two small artificial languages are acquired by an SRN. Among the interesting results he obtained, it is of special interest to us the fact that in his simulation the very same network was able to separately represent the lexical knowledge of each language. Another result was that within each language cluster, word classes (i.e., subject nouns, verbs, and object nouns) were correctly identified and categorized. This was interpreted as an indication of the sensitivity of the system to the statistical regularities of the input, namely, the transitional dependencies present in word co-occurrence patterns in sentences. Clearly, French’s (1998) is a step towards a richer understanding of the dynamics underlying bilingual knowledge and organization. In special, it is a step towards understanding how lexical and structural bilingual distinctions are accomplished through time, much in the way posited by Elman (1990). In this connection, a question can be posed: what if syntactical complexity is added to the grammar of the model, so that languages are distinguished not only by their lexicons, but also by their different structures? It is hoped that by adding syntactical complexity light will be shed on the mechanisms underlying bilingual knowledge. Computational simulations are of interest in connection with questions like this, because they offer the chance of manipulating variables believed to be active during bilingual language acquisition, but which cannot be controlled for in human data gathering. The Simulation An SRN was trained on two small artificial languages, just like in French (1998). For each of the languages, 4 adjectives were (optionally) added in the subject position. Thus, for each language, 16 words of were used (Table 1), resulting in 4 different syntactic structures (Table 2; notice that both objects and adjectives were optional). The intended syntactical difference between languages is thus brought about by differences in adjectives position: they always occurred before nouns in alpha and after them in beta (which, for mnemonic reasons, are referred to, respectively, as English and French). It must be noted that