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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 – 12 of 12 1 When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? ... Mao, Zhuoyuan; Chu, Chenhui; Dabre, Raj. - : arXiv, 2022 BASE Show details 2 IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages ... Kumar, Aman; Shrotriya, Himani; Sahu, Prachi. - : arXiv, 2022 BASE Show details 3 Extremely low-resource neural machine translation for Asian languages [<Journal>] Rubino, Raphael [Verfasser]; Marie, Benjamin [Verfasser]; Dabre, Raj [Verfasser]. DNB Subject Category Language Show details 4 IndicBART: A Pre-trained Model for Natural Language Generation of Indic Languages ... Dabre, Raj; Shrotriya, Himani; Kunchukuttan, Anoop. - : arXiv, 2021 BASE Show details 5 Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages ... Kanojia, Diptesh; Dabre, Raj; Dewangan, Shubham; Bhattacharyya, Pushpak; Haffari, Gholamreza; Kulkarni, Malhar. - : arXiv, 2021 Abstract: Cognates are variants of the same lexical form across different languages; for example 'fonema' in Spanish and 'phoneme' in English are cognates, both of which mean 'a unit of sound'. The task of automatic detection of cognates among any two languages can help downstream NLP tasks such as Cross-lingual Information Retrieval, Computational Phylogenetics, and Machine Translation. In this paper, we demonstrate the use of cross-lingual word embeddings for detecting cognates among fourteen Indian Languages. Our approach introduces the use of context from a knowledge graph to generate improved feature representations for cognate detection. We, then, evaluate the impact of our cognate detection mechanism on neural machine translation (NMT), as a downstream task. We evaluate our methods to detect cognates on a challenging dataset of twelve Indian languages, namely, Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam. Additionally, we create evaluation datasets ... : Published at COLING 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2112.08789 https://arxiv.org/abs/2112.08789 BASE Hide details 6 A Comprehensive Survey of Multilingual Neural Machine Translation ... Dabre, Raj; Chu, Chenhui; Kunchukuttan, Anoop. - : arXiv, 2020 BASE Show details 7 Softmax Tempering for Training Neural Machine Translation Models ... Dabre, Raj; Fujita, Atsushi. - : arXiv, 2020 BASE Show details 8 Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages ... The 28th International Conference on Computational Linguistics 2020; ., Gholamreza; Bhattacharyya, Pushpak. - : Underline Science Inc., 2020 BASE Show details 9 JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation ... Mao, Zhuoyuan; Cromieres, Fabien; Dabre, Raj. - : arXiv, 2020 BASE Show details 10 Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation ... Imankulova, Aizhan; Dabre, Raj; Fujita, Atsushi. - : arXiv, 2019 BASE Show details 11 MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language Processing ... Dabre, Raj; Kurohashi, Sadao. - : arXiv, 2017 BASE Show details 12 Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages ... Dabre, Raj; Cromieres, Fabien; Kurohashi, Sadao. - : arXiv, 2017 BASE Show details

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