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1
How Well Do LSTM Language Models Learn Filler-gap Dependencies?
In: Proceedings of the Society for Computation in Linguistics (2022)
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2
Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation ...
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3
A Resource for Computational Experiments on Mapudungun ...
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4
Language Technologies for Humanitarian Aid ...
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5
Language Technologies for Humanitarian Aid ...
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6
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ...
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7
ParaMor: Finding Paradigms across Morphology ...
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8
ParaMor: Minimally-Supervised Induction of Paradigm Structure and Morphological Analysis ...
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9
ParaMor: Finding Paradigms across Morphology ...
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10
POS error detection in automatically annotated corpora
Rehbein, Ines [Verfasser]; Levin, Lori [Herausgeber]; Stede, Manfred [Herausgeber]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2016
DNB Subject Category Language
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11
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning ...
Abstract: We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences---a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually. ... : Proceedings of NAACL 2016; 10 pages ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1605.03832
https://arxiv.org/abs/1605.03832
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12
Proceedings of the Grammar Engineering Across Frameworks (GEAF) 2015 Workshop
Bender, Emily,; Levin, Lori; Müller, Stefan. - : HAL CCSD, 2015. : Association for Computational Linguistics, 2015
In: The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01181344 ; The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Jul 2015, Beijing, China. Association for Computational Linguistics, 2015 (2015)
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13
Statistical modality tagging from rule-based annotations and crowdsourcing ...
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14
Use of Modality and Negation in Semantically-Informed Syntactic MT ...
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15
STTS 2.0? Improving the tagset for the part-of-speech-tagging of German spoken data
Westpfahl, Swantje [Verfasser]; Levin, Lori [Herausgeber]; Stede, Manfred [Herausgeber]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2014
DNB Subject Category Language
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16
A Modality Lexicon and its use in Automatic Tagging ...
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17
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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18
Use of Modality and Negation in Semantically-Informed Syntactic MT
In: DTIC (2012)
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19
Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing
Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 2012
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20
Use of Modality and Negation in Semantically-Informed Syntactic MT
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