Grammar Engineering for the Ekegusii Language in Grammatical Framework

The knowledge-driven economy uses technology, thereby increasing the demand for language tools and resources to acquire and distribute the knowledge. Such tools and resources are scarce for the under resourced, spoken Bantu languages. This paper develops a computational grammar for the Ekegusii language in the Grammatical Framework (GF) to bridge the gap. The grammar development uses a bottom-up and modular-driven approach. A machine translation experiment was set up to evaluate the grammar resulting in BLEU and PER of 55.95% and 19.49%, respectively. This work contributes by providing computational grammar for an under-resourced language, thus providing a platform for analysis and synthesis, plus a machine translation within the GF ecosystem.

The acquisition of UDCs

This chapter focuses on behavioral evidence concerning acquisition of unbounded dependency constructions and island phenomena. It provides a general overview of their acquisitional trajectory in normal developing L1, focusing on the emergence of filler-gap dependencies from one-word constructions to multi-clausal constructions, as well as island effects. The chapter argues that the gradual and frequency-based developmental evidence is consistent with an exemplar-based approach that contains rich morphosyntactic, semantic, and pragmatic information along the lines of the empiricist perspective. In particular, the extant evidence supports a usage-based view in which the growth of a grammar proceeds from simple units to more complex ones, exploiting the frequency of simple as well as complex exemplars. The chapter concludes by describing a exemplar- and chunk-based account of grammar development, composed of rich information that is probabilistic in nature, and shaped by experience.

INITIAL PROFICIENCY AND L2 GRAMMAR DEVELOPMENT DURING SHORT-TERM IMMERSION ABROAD

The first goal of this study was to examine how individual differences in initial L2 proficiency help explain L2 grammar development in oral production during short-term immersion abroad. The second goal of the study was methodological, and evaluated challenges that can result from operationalizing learners’ initial L2 proficiency as pretest performance on outcome measures (as opposed to independent proficiency measures) in analyses of L2 change. L2 Spanish learners participating in summer study abroad completed an elicited imitation task and two oral production tasks. Production data were analyzed for changes in relevant grammatical complexity and accuracy dimensions. Results indicate that learners with higher initial L2 proficiency experience greater L2 grammar advancement from short-term immersion, and that pretest performance can be an unreliable operational estimate of initial proficiency when analyzing L2 gains. We discuss findings following cognitive accounts of SLA, and highlight methodological implications for further research in immersion contexts and beyond.

ON THE SCOPE OF OUTPUT IN SLA

Following calls for more modality-sensitive perspectives of SLA, this study investigated the extent to which (a) producing the second language (L2) in the oral modality impacts learner-generated noticing and L2 development of grammatical structures embedded in subsequent auditory input, and whether (b) engaging in L2 production and input processing in the written modality differentially contributes to learner noticing and L2 outcomes compared to the oral modality. Participants were beginner-level L2 Spanish learners assigned to one of three pedagogic task conditions (No-output, Speaking, Writing). Two target structures differing in their relative intrinsic salience were considered in the study. Learners’ noticing behaviors were gauged using stimulated recall protocols, and L2 grammar development was measured using pre-, post-, and delayed posttests of production and written and aural acceptability judgment. Results revealed that engaging in oral output promoted greater noticing and deeper analysis of auditory input as well as more robust L2 grammar development compared to no output. However, sustained linguistic gains on the lower-salience target structure were only observed among participants who engaged in output and input processing in the written modality.

Targeting Turkish-to-English Interlingual Interference Through Context-Heavy Data-Driven Learning

This study investigates the effect of grammar-focused hands-on in-class data-driven learning (DDL) with a heavily contextualized corpus on the frequency of written errors attributable to common interlingual interference issues in low–intermediate Turkish learners ( n = 30) of English. Items representing the most common Turkish-to-English interlingual errors were selected through a two-step process involving the analysis of past studies and a subsequent ranking survey of teachers ( n = 10) of Turkish learners of English. Participants’ grammar development in terms of types of written errors was measured over a ten-week period through written tasks in a pre/posttest design, producing 19,328 words for analysis. The results, although variable by item, suggest that targeted DDL with the TED Corpus Search Engine (TCSE) helps reduce written errors in Turkish learners of English to a significant degree with a moderate effect size. Consequently, the investigation of DDL with the TCSE for the targeting of interlingual interference in other first-language contexts is recommended.