Computational Investigations of Multiword Chunks inￂ Language Learning

Second-language learners rarely arrive at native proficiency in a number of linguistic domains, including morphological and syntactic processing. Previous approaches to understanding the different outcomes of first- versus second-language learning have focused on cognitive and neural factors. In contrast, we explore the possibility that children and adults may rely on different linguistic units throughout the course of language learning, with specific focus on the granularity of those units. Following recent psycholinguistic evidence for the role of multiword chunks in online language processing, we explore the hypothesis that children rely more heavily on multiword units in language learning than do adults learning a second language. To this end, we take an initial step toward using large-scale, corpus-based computational modeling as a tool for exploring the granularity of speakers' linguistic units. Employing a computational model of language learning, the Chunk-Based Learner, we compare the usefulness of chunk-based knowledge in accounting for the speech of second-language learners versus children and adults speaking their first language. Our findings suggest that while multiword units are likely to play a role in second-language learning, adults may learn less useful chunks, rely on them to a lesser extent, and arrive at them through different means than children learning a first language. McCauley & Christiansen employ computational modeling to explore the hypothesis that children rely more than adults on multiword units in learning language. Using the Chunk-Based Learner model, they compare the usefulness of chunk-based knowledge in accounting for the speech of children learning their L1, native-speaking adults, and adult L2 learners. Their findings show that while multiword units play an important role in all populations, adult L2 learners rely on them to a lesser extent.