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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages ... Dhamecha, Tejas Indulal; Murthy, Rudra; Bharadwaj, Samarth. - : arXiv, 2021 BASE Show details 2 Multilingual and code-switching ASR challenges for low resource Indian languages ... Diwan, Anuj; Vaideeswaran, Rakesh; Shah, Sanket. - : arXiv, 2021 BASE Show details 3 Topic Transferable Table Question Answering ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bharadwaj, Samarth; Canim, Mustafa. - : Underline Science Inc., 2021 BASE Show details 4 Role of Language Relatedness in Multilingual Fine­-tuning of Language Models: A Case Study in Indo-­Aryan Languages ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bharadwaj, Samarth; Bhattacharyya, Pushpak; Dhamecha, Tejas; Murthy V, Rudra; Sankaranarayanan, Karthik. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.675/ Abstract: We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tuned on individual languages. A first of its kind detailed study is presented to track performance change as languages are added to a base language in a graded and greedy (in the sense of best boost of performance) manner; which reveals that careful selection of subset of related languages can significantly improve performance than utilizing all related languages. The Indo-Aryan (IA) language family is chosen for the study, the exact languages being Bengali, Gujarati, Hindi, Marathi, Oriya, Punjabi and Urdu. The script barrier is crossed by simple rule-based transliteration of the text of all languages to Devanagari. ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing URL: https://dx.doi.org/10.48448/f3t0-wg63 https://underline.io/lecture/38081-role-of-language BASE Hide details

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