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1
AUTOLEX: An Automatic Framework for Linguistic Exploration ...
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When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
In: NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.inria.fr/hal-03251105 ; NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2021, Mexico City, Mexico (2021)
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SD-QA: Spoken Dialectal Question Answering for the Real World ...
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SD-QA: Spoken Dialectal Question Answering for the Real World ...
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5
Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties ...
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6
Machine Translation into Low-resource Language Varieties ...
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7
Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors ...
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8
Systematic Inequalities in Language Technology Performance across the World's Languages ...
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9
Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling ...
Abstract: Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU). In the context of zero-shot learning, this task is typically approached by either using representations from pre-trained multilingual transformers such as mBERT, or by machine translating the source data into the known target language and then fine-tuning. Our work focuses on a particular scenario where the target language is unknown during training. To this goal, we propose a novel method to augment the monolingual source data using multilingual code-switching via random translations to enhance a transformer's language neutrality when fine-tuning it for a downstream task. This method also helps discover novel insights on how code-switching with different language families around the world impact the performance on the target language. Experiments on the benchmark dataset of MultiATIS++ yielded an average improvement of +4.2% in accuracy for intent task and +1.8% in F1 for slot ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://arxiv.org/abs/2103.07792
https://dx.doi.org/10.48550/arxiv.2103.07792
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10
Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering ...
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11
Towards More Equitable Question Answering Systems: How Much More Data Do You Need? ...
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12
Cross-Lingual Text Classification of Transliterated Hindi and Malayalam ...
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13
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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14
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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15
Towards more equitable question answering systems: How much more data do you need? ...
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16
Lexically Aware Semi-Supervised Learning for OCR Post-Correction ...
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17
When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection ...
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18
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ...
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19
Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
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20
AlloVera: a multilingual allophone database
In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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