Vowel deletion is frequent in the Chichicastenango dialect of K’iche’ (Maya). Whereas deletion in content words is reportedly predictable based on vowel quality, syllable structure and stress, deletion in function words is much more variable. This article investigates vowel deletion in a corpus of spontaneous, monologic speech. The results show that deletion in content words is highly regular, occurring to lax vowels in unstressed, CV syllables adjacent to the stressed syllable. A difference can be observed between vowels belonging to stress domain internal morphemes and extrametrical morphemes. Deletion in extrametrical morphemes is somewhat less regular, and does not occur in word-final syllables. In function words, vowel deletion is sensitive to similar conditions to those that affect content words, but is highly variable and is influenced by the phrase-level context.

Continuous immersion in a second language causes speakers’ first language to change, a phenomenon known as L1 attrition. We explored (1) whether bilingual native Mandarin speakers display attrition-related changes in their use of referring expressions in Mandarin after exposure to English and (2) whether the severity of attrition is affected by the amount of exposure to both Mandarin (L1) and English (L2) and English proficiency. All participants completed a questionnaire to assess their language experience and a picture description task in spoken Mandarin. The results show that where more monolingual Mandarin speakers preferred null pronouns, bilingual speakers tended to use overt pronouns, suggesting attrition-related changes in their native language which favoured explicitness. Our study also shows that decreased use of L1 coupled with increased use of L2 and higher L2 proficiency are likely to result in a greater degree of attrition, although such an association is statistically unreliable in some models.

In this research agenda, we first review the thematic landscape of task engagement research, providing definitions and elaborating on the core theoretical infrastructure for task engagement. We then summarize consensus perspectives from this body of work and identify important contributions that task engagement research stands to make to second language (L2) learning and teaching research. Following this, we outline five key research tasks that we believe will broaden the field’s understanding of task engagement, sharpen insights from empirical work, and accelerate the contribution of this research. Our goals are, first, to highlight for readers the shared understandings that exist in this important area of language learning research and, second, to draw attention to specific areas where additional L2 task engagement research is needed to push the field forward productively.

The volumes of historical data locked behind unstructured formats have long been a challenge for researchers in the computational humanities. While optical character recognition (OCR) and natural language processing have enabled large-scale text mining projects, the irregular formatting, inconsistent terminology and evolving printing practices complicate automated parsing and information extraction efforts for historical documents. This study explores the potential of large language models (LLMs) in processing and structuring irregular and non-standardized historical materials, using the U.S. Department of Agriculture’s Plant Inventory books (1898–2008) as a test case. Given the frequent evolution of these historical records, we implemented a pipeline combining OCR, custom segmentation rules and LLMs to extract structured data from the scanned texts. It provides an example of how incorporating LLMs into data-processing pipelines can enhance the accessibility and usability of historical and archival materials for scholars.

The present study compares the use of morphological case for argument interpretation between German L1 speakers in Norway and Germany to investigate whether and how processing may be affected by attrition. Participants were presented with a spoken sentence and pictures of two scenes, one showing an event as described by a transitive or ditransitive sentence and another showing the same event, with the roles of agent and patient (transitives) or recipient and theme (ditransitives) reversed. Their task was to select the scene that matched the sentence. End-of-sentence responses show no between-group differences in comprehension. Moreover, eye movements show that both groups exploit case marking on the first noun phrase in transitive sentences in the same way. However, differences in processing between groups emerge for the use of case marking on the first object following a ditransitive verb. While the home country group shows a higher likelihood of looks to the target after a dative-marked article than after an accusative-marked article prior to the second object, the reverse holds for the expat group, at least temporarily. Altogether, the results indicate subtle changes in the processing of alternating argument orders in ditransitive sentences in L1 German, potentially as a result of the bi-/multilingual experience.

This study developed and evaluated an online English speaking training approach that integrates corpora and artificial intelligence (AI) tools. The training integrated a self-developed spoken corpus, generative AI tools, and text-to-speech AI tools. Pre- and post-test results identified improvements in participants’ speaking performances. Participants attempted to use more positive linguistic features (e.g. producing complex sentences more frequently) and avoid using negative linguistic features (e.g. reducing the number of vowel errors) after receiving the training. Participants showed positive attitudes towards this corpus-based and AI-integrated English oral ability learning approach and affirmed the importance of integrating both tools. The corpus helped raise participants’ awareness of features that influence speaking performance and offered prompt engineering and feedback-checking functions, while the generative AI tools provided useful feedback and tailor-made sample responses. Additionally, text-to-speech AI tools offered learners with tailor-made native speaker samples for imitation and helped learners learn pausing. Results also revealed that this approach helped create an interactive oral ability learning environment, and the combination of corpora and AI tools provided more accurate feedback for each subskill of speaking.