Bharath Kumar Bolla

One of the major challenges in Educational NLP is to assess student responses to questions. Automatic short answer grading (ASAG) is the task of assessing short natural language responses questions using computational methods. Short Answers tests the ability of the student’s recall using natural language, unlike multiple-choice questions that evaluate only recognition. However, there is no easy way to evaluate short answers, resulting in manual evaluation and feedback putting an enormous burden on teachers in resource constraint countries like India. In this paper, we are presenting our research into this aspect by combining conventional and advanced Natural Language Processing (NLP) methods which include various embeddings that range from word level, sentence level and contextual. We are also presenting a comprehensive evaluation of various embedding techniques (word2vec, FastText, ELMo, Skip-Thoughts, Quick-Thoughts, FLAIR embeddings, InferSent, Google’s Universal Sentence Encoder and BERT) with respect to short text similarity. Our method helps in evaluating natural language-based short answers resulting in instant feedback for students. We also combined Explainable AI (XAI) insights on the logic behind scoring an answer thus providing feedback and improving our models.