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1
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
In: https://hal.inria.fr/hal-03540069 ; 2022 (2022)
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2
Findings of the IWSLT 2020 Evaluation campaign
Niehues, Jan; Federico, Marcello; Ma, Xutai. - : Association for Computational Linguistics, 2022
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3
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning
Nguyen, Thai-Son; Zenkel, Thomas; Waibel, Alex. - : Association for Computational Linguistics, 2022
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4
Tutorial: End-to-End Speech Translation
Negri, Matteo; Salesky, Elizabeth; Turchi, Marco. - : Association for Computational Linguistics, 2022
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5
Assessing Evaluation Metrics for Speech-to-Speech Translation ...
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6
Assessing Evaluation Metrics for Speech-to-Speech Translation ...
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7
The Multilingual TEDx Corpus for Speech Recognition and Translation ...
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8
Tutorial: End-to-End Speech Translation ...
Niehues, Jan; Salesky, Elizabeth; Turchi, Marco. - : Association for Computational Linguistics, 2021
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9
A surprisal--duration trade-off across and within the world's languages ...
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10
Assessing Evaluation Metrics for Speech-to-Speech Translation
In: 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (2021)
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11
Robust Open-Vocabulary Translation from Visual Text Representations ...
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12
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
Abstract: A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems' ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed ... : 39 pages, SIGMORPHON ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2006.11572
https://arxiv.org/abs/2006.11572
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13
SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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14
Findings of the IWSLT 2020 Evaluation campaign ...
Ansari, Ebrahim; Axelrod, Amittai; Bach, Nguyen. - : Association for Computational Linguistics, 2020
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15
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing ...
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16
A Corpus for Large-Scale Phonetic Typology ...
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17
A Corpus for Large-Scale Phonetic Typology ...
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18
A corpus for large-scale phonetic typology
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19
A Corpus for Large-Scale Phonetic Typology
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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20
A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners
In: DTIC (2013)
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