Theses and Dissertations

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    Automatic Text Translation of Multilingual Sentences using Transformer
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2022) Hari Charan, Edara Veera Venkata; Joshi, Manjunath V.; Hati, Avik
    Machine translation from one language to another is a complex problem in machine learning and one in which the machine still cannot achieve satisfactory results. The recent focus for solving this challenge has been on neural machine translation (NMT) techniques, by using architectures such as recurrent neural network (RNN) and long term short memory (LSTM). But the architecture of transformer is able to outperform these NMT techniques. The architecture of the transformer has been successfully utilized to build models that target a single language pair translation or translation among multiple languages. But it currently lacks research in the area of translation of multilingual sentences, where each sentence is in the form of a mixture of languages. In this work we will establish a model based on the transformer architecture that can translate multilingual sentences into a single language, with the help of a multilingual neural machine translation (MNMT) model and custom made datasets.
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    Learning to rank: using Bayesian networks
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Gupta, Parth; Mjumder, Prasenjit; Mitra, Suman K.
    Ranking is one of the key components of an Information Retrieval system. Recently supervised learning is involved for learning the ranking function and is called 'Learning to Rank' collectively. In this study we present one approach to solve this problem. We intend to test this problem in di erent stochastic environment and hence we choose to use Bayesian Networks for machine learning. This work also involves experimentation results on standard learning to rank dataset `Letor4.0'[6]. We call our approach as BayesNetRank. We compare the performance of BayesNetRank with another Support Vector Machine(SVM) based approach called RankSVM [5]. Performance analysis is also involved in the study to identify for which kind of queries, proposed system gives results on either extremes. Evaluation results are shown using two rank based evaluation metrics, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG).