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Research on corrective feedback has shown the beneficial effects of improving accuracy in writing though more research is being done on the effectiveness of different types of corrective feedback and the intervening variables. In line with this trend of research, this study was designed to investigate the effects of written and oral meta-linguistic feedback on the accuracy of subject-verb agreement in the writings of 47 undergraduate students. There were three groups in the study: Written Group, Oral Group and Interactional Group. The Written Group received direct error correction in combination with written meta-linguistic feedback. The Oral Group received direct error correction in combination with oral meta-linguistic feedback and the Interactional Group received direct error correction in combination with oral meta-linguistic feedback and also was involved in an interactional activity (discussion on their errors). The results demonstrated that all three groups improved their writing accuracy in the post-test as the result of receiving meta-linguistic feedback, but the Oral Group outperformed the other two groups. The findings of this study provided further evidence in support of the significant effects of corrective feedback especially oral meta-linguistic feedback which is both practical and time-saving.

With the extensive application of artificial intelligence technology in the tourism industry, robot-assisted tourism has become a vital strategy for enhancing tourist experiences and promoting sustainable tourism practices. This study aims to explore the impact of language feedback from tourism robots on tourists’ ecotourism behavior and analyze potential mediating and moderating mechanisms. Through three experimental studies, we found that robot guides with language feedback capabilities significantly improve tourists’ ecotourism behavior. Specifically, environmental responsibility acts as a moderator between the robot’s language feedback and tourists’ ecotourism behavior, indicating that the robot’s language feedback is more effective when tourists have a higher sense of environmental responsibility. Furthermore, the robot’s language feedback enhances tourists’ environmental awareness and responsibility by increasing cognitive trust and feedback propensity. The findings have practical implications for tourism destinations and operators in designing and implementing intelligent tourism services to promote tourists’ ecological engagement.

Introducing new techniques to improve learner-teacher interaction receives substantial attention from the field of educational psychology. The current research aims to investigate the effect of teacher feedback on the written English accuracy of English as Second Language (ESL) students. In a quasi-experimental study, 90 participants were given three distinct forms of feedback (i.e. oral meta-linguistic, also called direct feedback; written indirect feedback and no feedback) for writing errors of three types (i.e. verb tenses, use of articles and prepositions) and were assessed three times during the study (i.e., pre-test, immediate post-test and delayed test). One-way ANOVA demonstrated that the ESL learners given direct meta-linguistic oral feedback reported fewer errors in two out of the three linguistic forms in subsequent writing in comparison with ESL learners who received indirect written feedback and those who did not receive feedback. Findings suggest that introducing oral meta-linguistic teacher feedback in the Pakistani language learning context can improve the English language learning of students.

Machine translation post-editing (MTPE) is becoming a common practice in translation industry. Proper collaboration with linguists is key to successful MT implementation in the translation workflow. At TransPerfect, we apply different procedures to make MTPE convenient and beneficial for translators, which are outlined in this article.

This study tried to investigate the possible effects of focused Meta-linguistic highlighted error feedback on grammatical accuracy of writing among Iranian intermediate EFL learners. After selecting 60 homogenous participants among 90 university students attending Ghaemshahr University and randomly assigning them to two intact groups of 30 students, the researcher exposed the participants of the experimental group to 12 sessions of the intended treatment. During the treatment sessions, the said individuals were exposed to focused meta-linguistic feedback upon the submission of the drafts of their essays; in comparison, the subjects in the control group, upon the submission of their essays, were corrected using traditional approaches. After the completion of the study and performing the due data analysis on the results, a significant progress was found among the subjects in the experimental group as compared to the control group. Index Terms--focused meta-linguistic feedback, written corrective feedback, essay writing

La postedició de traduccions automàtiques (PETA) és cada cop més una pràctica comú dins la indústria de la traducció. Una col·laboració apropiada amb els traductors és clau per implantar la TA amb èxit dins el procés de traducció. En aquest article expliquem els diferents procediments que apliquem a TransPerfect per fer de la PETA una eina pràctica i beneficiosa per als professionals de la traducció. Machine translation post-editing (MTPE) is becoming a common practice in translation industry. Proper collaboration with linguists is key to successful MT implementation in the translation workflow. At TransPerfect, we apply different procedures to make MTPE convenient and beneficial for translators, which are outlined in this article. La posedición de traducciones automáticas (PETA) está adquiriendo el carácter de práctica habitual en la industria de la traducción. La adecuada colaboración con los profesionales de la lengua es un elemento fundamental para la correcta implementación de la TA en el proceso de traducción. En este artículo se indican los diversos procedimientos que empleamos en TransPerfect para que la PETA les resulte práctica y ventajosa a los traductores.

In this study, the linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) proposed earlier by the authors is first introduced. The principles of α-level sets and backpropagation through time approach are also briefly discussed. We next employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFBDFS. With this training algorithm, our LIFBDFS indeed becomes a potential candidate in solving real-time modeling and prediction problems.

The consensus reaching process (CRP) is often utilized to mitigate conflicts among decision makers and help them reach an agreement. Almost all existing CRP models focused on obtaining a consensual complete ranking of alternatives. However, in many situations, it may be sufficient to classify alternatives into a number of ordinal classes rather than a complete ranking. To this end, this study proposes an ordinal classification-based CRP (OCCRP) with the aim to generate consensual ordinal classes of alternatives. Further, this study investigates the OCCRP in the large-scale multi-attribute group decision making context, in which flexible linguistic expression is used to model uncertain opinions of decision makers. In the OCCRP, an approach is proposed to manage flexible linguistic expression by transforming it into linguistic distribution assessment. Then, an opinion clustering and aggregation approach is used to deal with the assessment information of the large group. Next, an approach-based technique for order performance by similarity to ideal solution is used to obtain the ordinal classification of alternatives. Following this, a feedback adjustment mechanism is presented to help decision makers improve their consensus level on the ordinal classification of alternatives. Finally, a numerical example about grape wine grading problem and a comparative analysis are presented to demonstrate the validity of the proposal.

Speaking more than one language demands a language control system that allows bilinguals to correctly use the intended language adjusting for possible interference from the non-target language. Understanding how the brain orchestrates the control of language has been a major focus of neuroimaging research on bilingualism and was central to our original neurocognitive language control model (Abutalebi & Green, 2007). We updated the network of language control (Green & Abutalebi, 2013) and here review the many new exciting findings based on functional and structural data that substantiate its core components. We discuss the language control network within the framework of the adaptive control hypothesis (Green & Abutalebi, 2013) that predicts adaptive changes specific to the control demands of the interactional contexts of language use. Adapting to such demands leads, we propose, to a neural reserve in the human brain.