ABSTRACT
What has English for Academic Purposes (EAP) writing had to encounter during the Covid-19-induced distance education in addition to the common challenges of students' difficulties in the face of a lack of social collaboration? This study drew upon the theoretical framework of the Holistic Shepherd Leadership Approach (HSLA) to address these issues in a junior EAP course in a hybrid mode, and to examine learners' perceptions of their experiences of writing an opinion letter regarding Sustainable Development Goals (SDG). The researcher employed tests of entry and exit-level writing proficiency assessed in terms of quality and quantity, a semi-structured self-administered questionnaire, and stratified interviews to collect 34 participants' responses. The results showed positive pedagogical, cognitive, and affective results, linked with mixed conceptions regarding technical issues, and emerging challenges related to the holistic needs of different levels of learners. Implications and pedagogical strategies are offered. © 2022 Taiwan English for Specific Purposes Association. All rights reserved.
ABSTRACT
In recent years, dialogue system is booming and widely used in customer service system, and has achieved good results. Viewing the conversation records between users and real customer service, we can see that the user's sentences are mixed with questions about products and services, and chat with customer service. According to the experience of professionals, it is helpful in improving the user experience to mix some chats in customer service conversations. However, users' questions are expected to be answered, while chatting is expected to interact with customer service. In order to produce an appropriate response, the dialogue system must be able to distinguish these two intentions effectively. Dialog act is a classification that linguists define according to its function. We think this information will help distinguishing questioning sentences and chatting sentences. In this paper, we combine a published COVID-19 QA dataset and a COVID-19-topic chat dataset to form our experimental data. Based on the BERT (Bidirectional Encoder Representation from Transformers) model, we build a question-chat classifier model. The experimental results show that the accuracy of the configuration with dialog act embedding is 16% higher than that with only original statement embedding. In addition, it is found that conversation behavior types such as "Statement-non-opinion", "Signal-non-understanding" and "Appreciation" are more related to question sentences, while "Wh-Question", "Yes-No-Question" and "Rhetorical-Question" questions are more related to chat sentences. © 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.