Neural Machine Translation (NMT) is an ongoing technique for Machine Translation (MT) using enormous artificial neural network. It has exhibited promising outcomes and has shown incredible potential in solving challenging machine translation exercises. One such exercise is the best approach to furnish great MT to language sets with a little preparing information. In this work, Zero Shot Translation (ZST) is inspected for a low resource language pair. By working on high resource language pairs for which benchmarks are available, namely Spanish to Portuguese, and training on data sets (Spanish-English and English-Portuguese) we prepare a state of proof for ZST system that gives appropriate results on the available data. Subsequently the same architecture is tested for Sanskrit to Hindi translation for which data is sparse, by training the model on English-Hindi and Sanskrit-English language pairs. In order to prepare and decipher with ZST system, we broaden the preparation and interpretation pipelines of NMT seq2seq model in tensorflow, incorporating ZST features. Dimensionality reduction of word embedding is performed to reduce the memory usage for data storage and to achieve a faster training and translation cycles. In this work existing helpful technology has been utilized in an imaginative manner to execute our NLP issue of Sanskrit to Hindi translation. A Sanskrit-Hindi parallel corpus of 300 is constructed for testing. The data required for the construction of parallel corpus has been taken from the telecasted news, published on Department of Public Information, state government of Madhya Pradesh, India website.
Samasāmayik: A Parallel Dataset for Hindi-Sanskrit Machine Translation
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Geakminds Technologies Private Limited（Geakminds技术有限公司）；Indian Institute of Technology Roorkee（印度理工学院罗奥基）
Attention based Sequence to Sequence Learning for Machine Translation of Low Resourced Indic Languages -- A case of Sanskrit to Hindi
Chhotubhai Gopalbhai Patel Institute of Technology, Uka Tarsadia University（乌卡·塔萨迪亚大学乔图巴伊·戈帕尔巴伊·帕特尔理工学院）；Babumadhav Institute of Information Technology, Uka Tarsadia University（乌卡·塔萨迪亚大学巴布马德哈夫信息技术学院）
An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis
Indian Institute of Technology Delhi（印度理工学院德里分校）；Birla Institute of Technology and Science Pilani（比尔拉科学技术学院皮拉尼分校）；Midam Charitable Trust（米达姆慈善信托）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；University of New South Wales（新南威尔士大学）
A Federated Approach to Predicting Emojis in Hindi Tweets
University of Alberta（阿尔伯塔大学）；Georgia Institute of Technology（佐治亚理工学院）；Stanford University（斯坦福大学）；Santa Clara University（圣克拉拉大学）；DJ Sanghvi College of Engineering（DJ桑格维工程学院）；Simon Fraser University（西蒙弗雷泽大学）
BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis
Centre for Sensors, Instrumentation and Cyber-Physical System Engineering (Centre for SeNSE), Indian Institute of Technology Delhi（印度理工学院德里分校传感器、仪器仪表和网络物理系统工程中心（SeNSE中心））；RSL Quantum, FITT, IIT Delhi（印度理工学院德里分校FITT的RSL量子公司）
Tourism Question Answer System in Indian Language using Domain-Adapted Foundation Models
Department of Linguistics Banaras Hindu University（语言学系班加罗尔 Hindu 大学）；Department of Humanistic Studies Indian Institute of Technology (BHU)（人文科学系印度理工学院（BHU））；Department of Computer Science and Engineering Indian Institute of Technology Bhilai（计算机科学与工程系印度理工学院 Bhilai）
No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection
Dept. of CSE IIT Kharagpur, India（印度 IIT 卡哈拉格普尔分校计算机科学与工程系）；Dept. of CSE IIIT Delhi, India（印度德里信息技术学院计算机科学与工程系）；IBM Research Bangalore, India（印度班加罗尔 IBM 研究院）
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses
Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney（过渡人工智能研究组，数学与统计学学院，UNSW悉尼）；Department of Civil Engineering, National Institute of Technology, Rourkela, India（土木工程系，印度国家理工学院，拉尔凯拉）；Centre for Artificial Intelligence and Innovation, Pingla Institute, Sydney, Australia（人工智能与创新中心，Pingla研究所，悉尼，澳大利亚）
IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems
Intrinsic Lab, Centre for Sensors, Instrumentation and Cyber-Physical System Engineering (Centre for SeNSE), Indian Institute of Technology Delhi（印度理工学院德里分校传感器、仪器仪表和网络物理系统工程中心（传感中心）本征实验室）；RSL Quantum, FITT, IIT Delhi（印度理工学院德里分校 FITT 学院 RSL 量子实验室）
Exploratory As-Analyzed No-Detection of Culturally-Marked Predicate-Triggered PII Amplification in a Synthetic-English RAG Probe: A Predicate-Resource-Confounded Audit
Northeastern University（东北大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Southern Methodist University（南卫理公会大学）
L3Cube-IndicHeadline-ID: A Dataset for Headline Identification and Semantic Evaluation in Low-Resource Indian Languages
Department of Information Technology, PICT, Pune（信息科技系，PICT，普那）；Indian Institute of Technology Madras, Chennai（印度理工学院马德拉斯分校，钦奈）；L3Cube Labs, Pune（L3Cube实验室，普那）
