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1
How Universal is Genre in Universal Dependencies? ...
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2
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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3
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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5
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions ...
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6
Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
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7
Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts ...
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8
SemEval 2021 Task 12: Learning with Disagreement ...
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9
Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
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10
DaN+: Danish Nested Named Entities and Lexical Normalization ...
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11
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ...
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
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12
SemEval-2021 Task 12: Learning with Disagreements
Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca. - : Association for Computational Linguistics, 2021
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13
We Need to Consider Disagreement in Evaluation
Basile, Valerio; Fell, Michael; Fornaciari, Tommaso. - : Association for Computational Linguistics, 2021. : country:USA, 2021. : place:Stroudsburg, PA, 2021
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