Browsing by Author "Taparia, R."

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    Building Intelligent Embodied AI Agents for Asking Clarifying Questions
    (Institute of Electrical and Electronics Engineers Inc., 2023) Lakhtaria, D.; Chhabra, R.; Taparia, R.; Anand Kumar, M.
    Interactive embodied agents are intelligent agents present inside some virtual environment and interact with humans to do tasks we want them to simulate. The communication takes place in some verbal or non-verbal form. This kind of communication gives a better and richer experience and more possibility for users to be satisfied with the carried-out task. An intelligent agent analyzes the environment it is in and performs tasks with all its knowledge based on the interaction. The task is carried out in the game environment where the Agent resides. There are some challenges to an agent that it needs to address, as well as to understand the instruction given to it. The next challenge is asking for clarification if the instruction given is unclear. In this research paper, we have discussed the problems mentioned above using the dataset provided by the NeurIPS 2022 IGLU Challenge which is a labeled dataset. We have discussed the various approaches for the problem mentioned. © 2023 IEEE.
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    Generating Synthetic Text Data for Improving Class Balance in Personality Prediction
    (Springer Science and Business Media Deutschland GmbH, 2024) Lakhtaria, D.; L, D.H.; Chhabra, R.; Taparia, R.; Anand Kumar, M.A.
    The growing popularity of social media as a means of self-expression and self-discovery has sparked a heightened curiosity in utilizing the Myers–Briggs Type Indicator (MBTI) to investigate human personalities. Despite the increasing use of word-embedding techniques, machine learning algorithms, and imbalanced data-handling techniques to predict MBTI personality types, further research is needed to explore how these approaches can enhance the accuracy of the results. Our research aimed to use the GPT model to address the problem of class imbalance. We have implemented several machine learning models such as RCNN, LSTM, XGBoost, and Random Forest. We have also tried using two-word embedding including Word2Vec and GloVe Embedding. According to our findings, the approach we used can attain a considerably high F1-score, which is dependent on the selected model for the prediction and classification of MBTI personality. The ability to accurately predict and classify MBTI personality through our approach has the potential to improve our comprehension of MBTI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.