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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 ...
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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? ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.79 Abstract: Question answering (QA) in English has been widely explored, but multilingual datasets are relatively new, with several methods attempting to bridge the gap between high- and low-resourced languages using data augmentation through translation and cross-lingual transfer. In this project we take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. Specifically, we perform extensive analysis to measure the efficacy of few-shot approaches augmented with automatic translations and permutations of context-question-answer pairs. In addition, we make suggestions for future dataset development efforts that make better use of a fixed annotation budget, with a goal of increasing the language coverage of QA datasets and systems. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/b7pd-tt38
https://underline.io/lecture/25680-towards-more-equitable-question-answering-systems-how-much-more-data-do-you-needquestion
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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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