Research on Small Group Writing Activities Compared to Individual Flipped Model Through Whatsapp

Purpose: Investigate the ability of EFL learners’cohesion with small group writing activities compared to individual flipped instruction model through Whatsapp with individual writing activities Design/Method: A quasi-experimental study with a non-equivalent control group and a pre-test/post-test design was implemented to find any significant difference between the two combinations. The instrument of this study was a writing test. Findings: The findings revealed that the small group flipped classroom instruction model through Whatsapp with small group writing activities performed better than teaching cohesion with individual flipped instruction through Whatsapp with individual writing activities. Originality: Flipped classroom innovation has attracted English Language Teaching researchers’ attention to scrutinize its effectiveness.

A Comprehensive Guideline for Bengali Sentiment Annotation

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

Efficient Channel Attention Based Encoder–Decoder Approach for Image Captioning in Hindi

Image captioning refers to the process of generating a textual description that describes objects and activities present in a given image. It connects two fields of artificial intelligence, computer vision, and natural language processing. Computer vision and natural language processing deal with image understanding and language modeling, respectively. In the existing literature, most of the works have been carried out for image captioning in the English language. This article presents a novel method for image captioning in the Hindi language using encoder–decoder based deep learning architecture with efficient channel attention. The key contribution of this work is the deployment of an efficient channel attention mechanism with bahdanau attention and a gated recurrent unit for developing an image captioning model in the Hindi language. Color images usually consist of three channels, namely red, green, and blue. The channel attention mechanism focuses on an image’s important channel while performing the convolution, which is basically to assign higher importance to specific channels over others. The channel attention mechanism has been shown to have great potential for improving the efficiency of deep convolution neural networks (CNNs). The proposed encoder–decoder architecture utilizes the recently introduced ECA-NET CNN to integrate the channel attention mechanism. Hindi is the fourth most spoken language globally, widely spoken in India and South Asia; it is India’s official language. By translating the well-known MSCOCO dataset from English to Hindi, a dataset for image captioning in Hindi is manually created. The efficiency of the proposed method is compared with other baselines in terms of Bilingual Evaluation Understudy (BLEU) scores, and the results obtained illustrate that the method proposed outperforms other baselines. The proposed method has attained improvements of 0.59%, 2.51%, 4.38%, and 3.30% in terms of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores, respectively, with respect to the state-of-the-art. Qualities of the generated captions are further assessed manually in terms of adequacy and fluency to illustrate the proposed method’s efficacy.

Research on Small Group Writing Activities Compared to Individual Flipped Model Through WhatsApp

Purpose: Investigate the ability of EFL learners’cohesion with small group writing activities compared to individual flipped instruction model through Whatsapp with individual writing activities Design/Method: A quasi-experimental study with a non-equivalent control group and a pre-test/post-test design was implemented to find any significant difference between the two combinations. The instrument of this study was a writing test. Findings: The findings revealed that the small group flipped classroom instruction model through Whatsapp with small group writing activities performed better than teaching cohesion with individual flipped instruction through Whatsapp with individual writing activities. Originality: Flipped classroom innovation has attracted English Language Teaching researchers’ attention to scrutinize its effectiveness.

Ways to Maintain ELLs’ Cultural Diversity in Education - Taking Three New York Elementary Schools as Examples

English as a second language (ESL) education refers to teaching non-native English speakers English as a second language. The number of English language learners (ELLs) is increasing in the United States in recent decades because of globalization, including immigrants, international students, merchants, refugees, etc. One of ELLs’ main characters is their various cultural backgrounds. Teaching and maintaining a diverse class within a safe learning environment can benefit students both now and in the future. In this case, understanding ELLs’ diverse cultures and knowing how to maintain ELLs’ cultural diversity is a significant consideration in American ESL education nowadays. This study reviews the cultural diversity in American ESL education by analyzing three New York elementary schools. The author summarizes some critical ways to maintain ELLs’ cultural diversity from four aspects: (1) the background of American ESL education and cultural diversity; (2) cultural diversity in school; (3) cultural diversity in family; (4) cultural diversity in communities. Finally, the study indicates the significance of connections among schools, families, and communities and identifies some difficulties when maintaining cultural diversity in education.

A Transformer-Based Approach to Multilingual Fake News Detection in Low-Resource Languages

Fake news classification is one of the most interesting problems that has attracted huge attention to the researchers of artificial intelligence, natural language processing, and machine learning (ML). Most of the current works on fake news detection are in the English language, and hence this has limited its widespread usability, especially outside the English literate population. Although there has been a growth in multilingual web content, fake news classification in low-resource languages is still a challenge due to the non-availability of an annotated corpus and tools. This article proposes an effective neural model based on the multilingual Bidirectional Encoder Representations from Transformer (BERT) for domain-agnostic multilingual fake news classification. Large varieties of experiments, including language-specific and domain-specific settings, are conducted. The proposed model achieves high accuracy in domain-specific and domain-agnostic experiments, and it also outperforms the current state-of-the-art models. We perform experiments on zero-shot settings to assess the effectiveness of language-agnostic feature transfer across different languages, showing encouraging results. Cross-domain transfer experiments are also performed to assess language-independent feature transfer of the model. We also offer a multilingual multidomain fake news detection dataset of five languages and seven different domains that could be useful for the research and development in resource-scarce scenarios.

Enriching Conventional Ensemble Learner with Deep Contextual Semantics to Detect Fake News in Urdu

Increased connectivity has contributed greatly in facilitating rapid access to information and reliable communication. However, the uncontrolled information dissemination has also resulted in the spread of fake news. Fake news might be spread by a group of people or organizations to serve ulterior motives such as political or financial gains or to damage a country’s public image. Given the importance of timely detection of fake news, the research area has intrigued researchers from all over the world. Most of the work for detecting fake news focuses on the English language. However, automated detection of fake news is important irrespective of the language used for spreading false information. Recognizing the importance of boosting research on fake news detection for low resource languages, this work proposes a novel semantically enriched technique to effectively detect fake news in Urdu—a low resource language. A model based on deep contextual semantics learned from the convolutional neural network is proposed. The features learned from the convolutional neural network are combined with other n-gram-based features and are fed to a conventional majority voting ensemble classifier fitted with three base learners: Adaptive Boosting, Gradient Boosting, and Multi-Layer Perceptron. Experiments are performed with different models, and results show that enriching the traditional ensemble learner with deep contextual semantics along with other standard features shows the best results and outperforms the state-of-the-art Urdu fake news detection model.