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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 – 9 of 9 1 ATIS - Seven Languages Mansour, Saab; Haider, Batool. - : Linguistic Data Consortium, 2021. : https://www.ldc.upenn.edu, 2021 BASE Show details 2 Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer ... Xu, Weijia; Haider, Batool; Krone, Jason. - : arXiv, 2021 BASE Show details 3 Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings ... Alqahtani, Sawsan; Lalwani, Garima; Zhang, Yi. - : arXiv, 2021 BASE Show details 4 Nearest Neighbour Few-Shot Learning for Cross-lingual Classification ... Bari, M Saiful; Haider, Batool; Mansour, Saab. - : arXiv, 2021 BASE Show details 5 ATIS - Seven Languages ... Mansour, Saab; Haider, Batool. - : Linguistic Data Consortium, 2021 BASE Show details 6 Nearest Neighbour Few-Shot Learning for Cross-lingual Classification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bari, M Saiful; Haidar, Batool. - : Underline Science Inc., 2021 BASE Show details 7 Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Alqahtani, Sawsan; Lalwani, Garima; Mansour, Saab; Romeo, Salvatore; Zhang, Yi. - : Underline Science Inc., 2021 Abstract: Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further improve multilingual contextualized representations for downstream cross-lingual transfer. This approach does not require word-alignment pairs prior to fine-tuning that may lead to sub-optimal matching and instead learns the word alignments within context in an unsupervised manner. It also allows different types of mappings due to soft matching between source and target sentences. We benchmark our proposed method on two tasks (XNLI and XQuAD) and achieve improvements over baselines as well as competitive results compared to similar recent works. ... URL: https://dx.doi.org/10.48448/bxbc-e440 https://underline.io/lecture/38428-using-optimal-transport-as-alignment-objective-for-fine-tuning-multilingual-contextualized-embeddings BASE Hide details 8 End-to-End Slot Alignment and Recognition for Cross-Lingual NLU ... Xu, Weijia; Haider, Batool; Mansour, Saab. - : arXiv, 2020 BASE Show details 9 A comparison of segmentation methods and extended lexicon models for Arabic statistical machine translation Ney, Hermann; Hasan, Saša; Mansour, Saab In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 26 (2012) 1-2, 47-65 BLLDB OLC Linguistik Show details

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