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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 Unsupervised Translation of German--Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language ... Edman, Lukas; Üstün, Ahmet; Toral, Antonio. - : arXiv, 2021 BASE Show details 2 On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions ... van der Goot, Rob; Üstün, Ahmet; Plank, Barbara. - : arXiv, 2021 BASE Show details 3 Multilingual Unsupervised Neural Machine Translation with Denoising Adapters ... Üstün, Ahmet; Bérard, Alexandre; Besacier, Laurent. - : arXiv, 2021 BASE Show details 4 Multilingual Unsupervised Neural Machine Translation with Denoising Adapters ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berard, Alexandre; Besacier, Laurent. - : Underline Science Inc., 2021 BASE Show details 5 From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ... van der Goot, Rob; Sharaf, Ibrahim; Imankulova, Aizhan; Üstün, Ahmet; Stepanović, Marija; Ramponi, Alan; Khairunnisa, Siti Oryza; Komachi, Mamoru; Plank, Barbara. - : arXiv, 2021 Abstract: The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, it is desirable to reuse existing data in high-resource languages to develop models for low-resource scenarios. We introduce xSID, a new benchmark for cross-lingual Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect. To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. We study two setups which differ by type and language coverage of the pre-trained embeddings. Our results show that jointly learning the main tasks with masked language modeling is effective for slots, while machine translation transfer works best for intent classification. ... : To appear in the proceedings of NAACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2105.07316 https://dx.doi.org/10.48550/arxiv.2105.07316 BASE Hide details 6 On the Difficulty of Translating Free-Order Case-Marking Languages ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bisazza, Arianna; Sportel, Stephan. - : Underline Science Inc., 2021 BASE Show details 7 UDapter: Language Adaptation for Truly Universal Dependency Parsing ... Üstün, Ahmet; Bisazza, Arianna; Bouma, Gosse. - : arXiv, 2020 BASE Show details 8 FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings ... Braaksma, Bertelt; Scholtens, Richard; van Suijlekom, Stan. - : arXiv, 2020 BASE Show details 9 Incorporating word embeddings in unsupervised morphological segmentation Üstün, Ahmet; Can, Burcu In: 2020 ; 1 ; 21 (2020) BASE Show details 10 Characters or morphemes: how to represent words? Üstün, Ahmet; Kurfalı, Murathan; Can, Burcu. - : Association for Computational Linguistics, 2018 BASE Show details 11 A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation ... Kurfalı, Murathan; Üstün, Ahmet; Can, Burcu. - : arXiv, 2017 BASE Show details 12 Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small Datasets ... Can, Burcu; Üstün, Ahmet; Kurfalı, Murathan. - : arXiv, 2017 BASE Show details

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