Natural Language Inference or NLI adds extraordinary advantages to already existing Natural Language Processing use cases available. NLI has the potential to add syllogistic reasoning over the already existing facts and develop a new kind of knowledge dataset in itself. In this paper, we have demonstrated a Recognizing Textual Entailment wherein the task is to recognize whether a given hypothesis is true (Entailment), false (Contradiction) or unrelated(neutral) with respect to the sentence called premise. The task is performed by training MNLI corpus along with the manually collected dataset from Amazon Product Reviews each having hypothesis and premise pairs with corresponding labels. With this use case, we propose to bring sustainable development in the classification methods used by major E-commerce companies.

For Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) systems, code-switching—the habit of alternately speaking in several languages within a single conversation—offers special difficulties and possibilities. ASR systems have to efficiently manage language transitions as multilingual communication gets more common if we want real-time speech recognition. This work investigates innovative approaches for processing code-switched audio, solves the dearth of multilingual datasets, and assesses several technologies applied to identify and analyze mixed-language speech. Emphasizing Hindi-Marathi code-switching, we present a dynamic language-switching architecture leveraging reinforcement learning methods including Q-Learning and Deep Q-Networks (DQN) to improve language transition identification. Moreover, we present a dataset especially meant for multilingual voice recognition and evaluate ASR performance with Character Error Rate (CER) and Word Error Rate (WER). Our study reveals current constraints and provides future directions to improve ASR adaptation, therefore guaranteeing more accurate and strong recognition in many multilingual settings.