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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 Page: 1 2 Hits 1 – 20 of 21 1 Interpreting Arabic Transformer Models ... Abdelali, Ahmed; Durrani, Nadir; Dalvi, Fahim. - : arXiv, 2022 BASE Show details 2 How transfer learning impacts linguistic knowledge in deep NLP models? ... Durrani, Nadir; Sajjad, Hassan; Dalvi, Fahim. - : arXiv, 2021 BASE Show details 3 How transfer learning impacts linguistic knowledge in deep NLP models? ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Dalvi, Fahim; Durrani, Nadir. - : Underline Science Inc., 2021 BASE Show details 4 Effect of Post-processing on Contextualized Word Representations ... Sajjad, Hassan; Alam, Firoj; Dalvi, Fahim. - : arXiv, 2021 BASE Show details 5 Similarity Analysis of Contextual Word Representation Models ... Wu, John M.; Belinkov, Yonatan; Sajjad, Hassan. - : arXiv, 2020 BASE Show details 6 AraBench: Benchmarking Dialectal Arabic-English Machine Translation ... The 28th International Conference on Computational Linguistics 2020; Abdelali, Ahmed; Dalvi, Fahim; Durrani, Nadir; SAJJAD, HASSAN. - : Underline Science Inc., 2020 Abstract: Low-resource machine translation suffers from the scarcity of training data and the unavailability of standard evaluation sets. While a number of research efforts target the former, the unavailability of evaluation benchmarks remain a major hindrance in tracking the progress in low-resource machine translation. In this paper, we introduce AraBench, an evaluation suite for dialectal Arabic to English machine translation. Compared to Modern Standard Arabic, Arabic dialects are challenging due to their spoken nature, non-standard orthography, and a large variation in dialectness. To this end, we pool together already available Dialectal Arabic-English resources and additionally build novel test sets. AraBench offers 4 coarse, 15 fine-grained and 25 city-level dialect categories, belonging to diverse genres, such as media, chat, religion and travel with varying level of dialectness. We report strong baselines using several training settings: fine-tuning, back-translation and data augmentation. The evaluation ... Keyword: Computer and Information Science; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/wbq8-xt62 https://underline.io/lecture/6389-arabench-benchmarking-dialectal-arabic-english-machine-translation BASE Hide details 7 A Clustering Framework for Lexical Normalization of Roman Urdu ... Khan, Abdul Rafae; Karim, Asim; Sajjad, Hassan. - : arXiv, 2020 BASE Show details 8 Analyzing Individual Neurons in Pre-trained Language Models ... Durrani, Nadir; Sajjad, Hassan; Dalvi, Fahim. - : arXiv, 2020 BASE Show details 9 On the Linguistic Representational Power of Neural Machine Translation Models Belinkov, Yonatan; Durrani, Nadir; Dalvi, Fahim... In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020) BASE Show details 10 On the Linguistic Representational Power of Neural Machine Translation Models ... Belinkov, Yonatan; Durrani, Nadir; Dalvi, Fahim. - : arXiv, 2019 BASE Show details 11 What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models ... Dalvi, Fahim; Durrani, Nadir; Sajjad, Hassan. - : arXiv, 2018 BASE Show details 12 Identifying and Controlling Important Neurons in Neural Machine Translation ... Bau, Anthony; Belinkov, Yonatan; Sajjad, Hassan. - : arXiv, 2018 BASE Show details 13 The Summa Platform Prototype ... Liepins, Renars; Germann, Ulrich; Barzdins, Guntis. - : Zenodo, 2017 BASE Show details 14 The Summa Platform Prototype ... Liepins, Renars; Germann, Ulrich; Barzdins, Guntis. - : Zenodo, 2017 BASE Show details 15 Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging ... Sajjad, Hassan; Dalvi, Fahim; Durrani, Nadir. - : arXiv, 2017 BASE Show details 16 The SUMMA Platform Prototype Riedel, Sebastian; Garner, Phil; Narayan, Shashi... In: http://infoscience.epfl.ch/record/233575 (2017) BASE Show details 17 Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015 ... Sajjad, Hassan; Durrani, Nadir; Guzman, Francisco. - : arXiv, 2016 BASE Show details 18 QCMUQ@QALB-2015 Shared Task: Combining Character level MT and Error-tolerant Finite-State Recognition for Arabic Spelling Correction ... Bouamor, Houda; Sajjad, Hassan; Durrani, Nadir. - : Carnegie Mellon University, 2015 BASE Show details 19 QCMUQ@QALB-2015 Shared Task: Combining Character level MT and Error-tolerant Finite-State Recognition for Arabic Spelling Correction ... Bouamor, Houda; Sajjad, Hassan; Durrani, Nadir. - : Carnegie Mellon University, 2015 BASE Show details 20 Hindi-to-Urdu machine translation through transliteration Durrani, Nadir; Sajjad, Hassan; Fraser, Alexander... In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 465-474 BLLDB Show details Page: 1 2

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