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1
Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation ...
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2
Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
In: Transactions of the Association for Computational Linguistics, 7, 313–325 ; ISSN: 2307-387X (2022)
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3
Lightly Supervised Quality Estimation
Waibel, Alex; Niehues, Jan; Stüker, Sebastian. - : Association for Computational Linguistics, 2022
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4
MasakhaNER: Named entity recognition for African languages
In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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5
Breaking Down Multilingual Machine Translation ...
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6
Measuring and Increasing Context Usage in Context-Aware Machine Translation ...
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7
Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
Mager, Manuel; Oncevay, Arturo; Ebrahimi, Abteen. - : Association for Computational Linguistics, 2021
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8
Explorations in Transfer Learning for OCR Post-Correction ...
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9
Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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10
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction ...
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11
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ...
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12
Explicit Alignment Objectives for Multilingual Bidirectional Encoders ...
NAACL 2021 2021; Firat, Orhan; Hu, Junjie. - : Underline Science Inc., 2021
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13
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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14
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ...
NAACL 2021 2021; Hauptmann, Alexander; Hu, Junjie. - : Underline Science Inc., 2021
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15
Distributionally Robust Multilingual Machine Translation ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.458/ Abstract: Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memoryefficiency of deployed models. However, the heavy data imbalance between languages hinders the model from performing uniformly across language pairs. In this paper, we propose a new learning objective for MNMT based on distributionally robust optimization, which minimizes the worst-case expected loss over the set of language pairs. We further show how to practically optimize this objective for large translation corpora using an iterated best response scheme, which is both effective and incurs negligible additional computational cost compared to standard empirical risk minimization. We perform extensive experiments on three sets of languages from two datasets and show that our method consistently outperforms strong baseline methods in terms of average and per-language ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
URL: https://dx.doi.org/10.48448/kr4y-4087
https://underline.io/lecture/37597-distributionally-robust-multilingual-machine-translation
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16
Multi-view Subword Regularization ...
NAACL 2021 2021; ., Sebastian; Neubig, Graham. - : Underline Science Inc., 2021
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17
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ...
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18
Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
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19
Phrase-level Active Learning for Neural Machine Translation ...
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20
Do Context-Aware Translation Models Pay the Right Attention? ...
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