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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
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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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Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties ...
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Machine Translation into Low-resource Language Varieties ...
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Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors ...
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Systematic Inequalities in Language Technology Performance across the World's Languages ...
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Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling ...
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Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering ...
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Towards More Equitable Question Answering Systems: How Much More Data Do You Need? ...
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Cross-Lingual Text Classification of Transliterated Hindi and Malayalam ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Towards more equitable question answering systems: How much more data do you need? ...
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Lexically Aware Semi-Supervised Learning for OCR Post-Correction ...
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When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection ...
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Abstract:
Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun "wall" has different lexical manifestations in Spanish -- "pared" refers to an indoor wall while "muro" refers to an outside wall. However, this variety of lexical distinction may not be obvious to non-native learners unless the distinction is explained in such a way. In this work, we present a method for automatically identifying fine-grained lexical distinctions, and extracting concise descriptions explaining these distinctions in a human- and machine-readable format. We confirm the quality of these extracted descriptions in a language learning setup for two languages, Spanish and Greek, where we use them to teach non-native speakers when to translate a given ambiguous word into its different possible translations. Code and data are publicly released here (https://github.com/Aditi138/LexSelection) ... : Accepted at EMNLP 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2109.06014 https://arxiv.org/abs/2109.06014
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When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ...
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Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
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AlloVera: a multilingual allophone database
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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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