The objective of this research is to conduct a systematic literature review, analyzing the influence of implementing the ChatGPT tool in the field of education. The data for this study was gathered through a systematic review of studies published sin
ce the launch of ChatGPT in November 2022. Three prominent educational databases (Web of Science, Taylor& Francis Online, Eric) were utilized for this purpose. The study incorporated a sample of 18 relevant studies, and a descriptive and quantitative methodology was employed to present the most noteworthy findings. The outcomes indicate that the incorporation of ChatGPT in the educational setting positively impacts the teaching and learning processes. Nevertheless, the results also shed light on topics such as factors that determine students' attitudes toward the application, positive and negative effects, and how to ensure academic integrity when applying AI in education. Despite ChatGPT's potential to enhance the educational experience, its successful integration hinges on educators being well-versed in its functionalities. These insights lay a robust foundation for future research endeavors and informed decision-making concerning the incorporation of ChatGPT in educational contexts.
This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian. We utilized different Transformer architectures
, pretraining and back-translation strategies to improve translation quality. Concretely, we explore mBART (Liu et al., 2020) to demonstrate the effectiveness of the pretraining strategy. Our submissions (Tencent AI Lab Machine Translation, TMT) in German/French/Spanish⇒English are ranked 1st respectively according to the official evaluation results in terms of BLEU scores.
This paper describes the submission of Huawei Translation Service Center (HW-TSC) to WMT21 biomedical translation task in two language pairs: Chinese↔English and German↔English (Our registered team name is HuaweiTSC). Technical details are introduced
in this paper, including model framework, data pre-processing method and model enhancement strategies. In addition, using the wmt20 OK-aligned biomedical test set, we compare and analyze system performances under different strategies. On WMT21 biomedical translation task, Our systems in English→Chinese and English→German directions get the highest BLEU scores among all submissions according to the official evaluation results.
سنتحدث في هذه الحلقة عن أليات البحث في غوغل مستخدمين استكشاف المعرفة داتا وتحسين الطريقة باستخدام المترادافات لمجال سيو (البحث الأمثلي)
Proofreading is the process of checking text to detect spelling, grammatical, and semantic errors in order to correct them, proofreading the grammar and the meaning of the natural languages is considered one of the basic objectives for people who a
re interested in computational linguistics, because it becomes necessary for checking written text on the computers in multiple areas, such as proofreading emails and texts on the websites pages, it is also essential for proofreading scientific articles and researches, and it can be used to correct students' answers in the traditional e-learning exams. In addition to that the manual correction process of students' answers in the traditional way is expensive in terms of time and effort, sometimes it is error prone, and it becomes more difficult when there are large number of students, so the automatic correction process is an important step to save time and effort and it avoids errors during correcting answers in the traditional way. This research presents the stages of building Automatic Content Verification Compiler. It presents the stages of building a system which is interested in English syntax check, and it displays the stages of the lexical analysis which is considered a first stage to execute the syntax analysis, in addition to that it shows the stages of executing the syntax analysis which builds the grammatical model, this model describes the simple sentences in English, the study depends on studying grammatical structure in English, then it suggests suitable parts of this model, and it presents an application which verifies English sentences and draws derivation tree of these sentences.
