Exploring Automated Essay Scoring for Nonnative English Speakers

Automated Essay Scoring (AES) has been quite popular and is being widely used. However, lack of appropriate methodology for rating nonnative English speakers' essays has meant a lopsided advancement in this field. In this paper, we report initial results of our experiments with nonnative AES that learns from manual evaluation of nonnative essays. For this purpose, we conducted an exercise in which essays written by nonnative English speakers in test environment were rated both manually and by the automated system designed for the experiment. In the process, we experimented with a few features to learn about nuances linked to nonnative evaluation. The proposed methodology of automated essay evaluation has yielded a correlation coefficient of 0.750 with the manual evaluation.Comment: Accepted for publication at EUROPHRAS 201

NLP and deep learning methods for curbing the spread of misinformation in India

The current fight against COVID-19 is not only around its prevention and cure but it is also about mitigating the negative impact resulting from misinformation around it. The pervasiveness of social media and access to smartphones has propelled the spread of misinformation on such a large scale that it is considered as one of the main threats to our society by the World Economic Forum. This ‘Infodemic’ has caused widespread rumors, fueled practices that can jeopardize one’s health, and has even resulted in hate violence in certain parts of the world. We built an engine that has the ability to match incoming text, which may contain correct or incorrect information, with a known repository of misinformation. By matching texts on embeddings generated using BERT, we evaluated paraphrased texts to see if they matched texts previously labeled as misinformation. Further, we augmented an existing data corpus of texts by tagging each misinformation with one or more impact categories. We may be able to take specific actions to avert the consequence of misinformation if we can predict the particular ramification of a certain type of misinformation