Question Answering (QA) has proved to be an arduous challenge in the area of natural language processing (NLP) and artificial intelligence (AI). Many attempts have been made to develop complete solutions for QA as well as improving significant submodules of the QA systems to improve overall performance through the course of time. Questions are the most important piece of QA, because knowing the question is equivalent to knowing what counts as an answer [1]. In this work, we have attempted to understand questions in a better way by using Quantum Machine Learning (QML). The properties of Quantum Computing (QC) have enabled classically intractable data processing. So, in this paper, we have performed question classification on questions from two classes of SelQA (Selection-based Question Answering) dataset using quantum-based classifier algorithms-quantum support vector machine (QSVM) and variational quantum classifier (VQC) from Qiskit (Quantum Information Science toolKIT) for Python. We perform classification with both classifiers in almost similar environments and study the effects of circuit depths while comparing the results of both classifiers. We also use these classification results with our own rule-based QA system and observe significant performance improvement. Hence, this experiment has helped in improving the quality of QA in general.

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Since the beginning of machine translation (MT) research, MT evaluation has been an area of interest of researchers. In literature, one can find more papers on MT evaluation than on machine translation itself. This paper describes the work done on developing our MT evaluation metric which incorporates linguistic as well as word embeddings for the evaluation of MT outputs. We have studied the performance of our metric on some English to Indian language machine translation systems. For this study, a comprehensive corpus was also developed which considered sentences based on different constructs. It was found that the proposed metric provides good results which are comparable with human (evaluation) judgments.