Aural lexical knowledge (ALK) is crucial for second language (L2) listening. Despite its importance, there is scant research that has validly explored the relationship between ALK and L2 listening across different English as a Foreign Language (EFL) contexts. In an effort to broaden this research base, the current study closely replicates a previous study, Cheng et al. (2022), which measured single‐word ALK, phrasal verb ALK and L2 listening comprehension among participants with Chinese as a first language (L1). The current study administered the same instruments but did so among 147 Japanese and 131 Arabic‐speaking English language learners. Results indicated that the capacity of ALK to predict variance in L2 listening for the Japanese group (R² = .38) was similar to that observed in the original study (R² = .42). However, the results for the Arabic‐speaking group were very different to that of the original study and showed an unexpectedly strong relationship between ALK and L2 listening (R² = .92). Future research directions and pedagogical implications are discussed.

Neural machine translation (NMT) has shown impressive performance when trained on large-scale corpora. However, generic NMT systems have demonstrated poor performance on out-of-domain translation. To mitigate this issue, several domain adaptation methods have recently been proposed which often lead to better translation quality than genetic NMT systems. While there has been some continuous progress in NMT for English and other European languages, domain adaption in Arabic has received little attention in the literature. The current study, therefore, aims to explore the effectiveness of domain-specific adaptation for Arabic MT (AMT), in yet unexplored domain, financial news articles. To this end, we developed a parallel corpus for Arabic-English (AR-EN) translation in the financial domain to benchmark different domain adaptation methods. We then fine-tuned several pre-trained NMT and Large Language models including ChatGPT-3.5 Turbo on our dataset. The results showed that fine-tuning pre-trained NMT models on a few well-aligned in-domain AR-EN segments led to noticeable improvement. The quality of ChatGPT translation was superior to other models based on automatic and human evaluations. To the best of our knowledge, this is the first work on fine-tuning ChatGPT towards financial domain transfer learning. To contribute to research in domain translation, we made our datasets and fine-tuned models available.

Although core in the teaching of academic language skills, little research to date has investigated what makes video‐recorded lectures difficult for language learners. As part of a larger program to develop automated videotext complexity measures, this study reports on selected dimensions of linguistic complexity to understand how they contribute to overall videotext difficulty. Based on the ratings of English language learners of 320 video lectures, we built regression models to predict subjective estimates of video lecture difficulty. The results of our analysis demonstrate that a 4‐component partial least square regression model explains 52% of the variance in video difficulty and significantly outperformed a baseline model in predicting the difficulty of videos in an out‐of‐sample testing set. The results of our study point to the use of linguistic complexity features for predicting overall videotext difficulty and raise the possibility of developing automated systems for measuring video difficulty, akin to those already available for estimating the readability of written materials.

Pidgin languages across the world are characterized by a universal set of linguistic features that, therefore, can be utilized for their identification. Among these linguistic traits, two are found in Gulf Arabic Pidgin (GPA); an overt reduction of sophisticated linguistic items and an increased regularity of less complicated linguistic items used to fulfill other linguistic functions (Nass, 2008; Bakir, 2010; Avram, 2014). The purpose of the present study is twofold: 1) to descriptively analyze some essential morphological (inflectional affixation) and syntactic features within the nominal and verbal systems of GPA, and 2) to statistically measure the level of the consistency among GPA speakers in their use of inflections, sentence structures, and navigation markers. The study starts with reviewing and discussing the literature on pidgins and the main linguistic characteristics of well-studied pidgin languages with the reference to the sociolinguistic environment in which this language variety is typically evoked. This is followed by a review on the few studies conducted on GPA. It ends with a discussion on the current linguistic status of GPA in light of the results obtained.