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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 19 of 19 1 AUTOLEX: An Automatic Framework for Linguistic Exploration ... Chaudhary, Aditi; Sheikh, Zaid; Mortensen, David R. - : arXiv, 2022 BASE Show details 2 Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties ... Siminyu, Kathleen; Li, Xinjian; Anastasopoulos, Antonios. - : arXiv, 2021 BASE Show details 3 Systematic Inequalities in Language Technology Performance across the World's Languages ... Blasi, Damián; Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2021 BASE Show details 4 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... Pratapa, Adithya; Anastasopoulos, Antonios; Rijhwani, Shruti. - : arXiv, 2021 BASE Show details 5 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 6 Lexically Aware Semi-Supervised Learning for OCR Post-Correction ... Rijhwani, Shruti; Rosenblum, Daisy; Anastasopoulos, Antonios. - : arXiv, 2021 BASE Show details 7 When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection ... Chaudhary, Aditi; Yin, Kayo; Anastasopoulos, Antonios. - : arXiv, 2021 BASE Show details 8 When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 9 Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Neubig, Graham. - : Underline Science Inc., 2021 BASE Show details 10 AlloVera: a multilingual allophone database Mortensen, David,; Li, Xinjian; Littell, Patrick... 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) BASE Show details 11 AlloVera: A Multilingual Allophone Database ... Mortensen, David R.; Li, Xinjian; Littell, Patrick. - : arXiv, 2020 BASE Show details 12 Automatic Extraction of Rules Governing Morphological Agreement ... Chaudhary, Aditi; Anastasopoulos, Antonios; Pratapa, Adithya. - : arXiv, 2020 BASE Show details 13 A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ... Neubig, Graham; Rijhwani, Shruti; Palmer, Alexis. - : arXiv, 2020 BASE Show details 14 Universal Phone Recognition with a Multilingual Allophone System ... Li, Xinjian; Dalmia, Siddharth; Li, Juncheng. - : arXiv, 2020 BASE Show details 15 X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models ... Jiang, Zhengbao; Anastasopoulos, Antonios; Araki, Jun. - : arXiv, 2020 BASE Show details 16 AlloVera: a multilingual allophone database Mortensen, David,; Li, Xinjian; Littell, Patrick... 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) BASE Show details 17 Generalized Data Augmentation for Low-Resource Translation ... Xia, Mengzhou; Kong, Xiang; Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2019 Abstract: Translation to or from low-resource languages LRLs poses challenges for machine translation in terms of both adequacy and fluency. Data augmentation utilizing large amounts of monolingual data is regarded as an effective way to alleviate these problems. In this paper, we propose a general framework for data augmentation in low-resource machine translation that not only uses target-side monolingual data, but also pivots through a related high-resource language HRL. Specifically, we experiment with a two-step pivoting method to convert high-resource data to the LRL, making use of available resources to better approximate the true data distribution of the LRL. First, we inject LRL words into HRL sentences through an induced bilingual dictionary. Second, we further edit these modified sentences using a modified unsupervised machine translation framework. Extensive experiments on four low-resource datasets show that under extreme low-resource settings, our data augmentation techniques improve translation quality ... : Accepted to ACL 2019 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1906.03785 https://dx.doi.org/10.48550/arxiv.1906.03785 BASE Hide details 18 Pushing the Limits of Low-Resource Morphological Inflection ... Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2019 BASE Show details 19 Should All Cross-Lingual Embeddings Speak English? ... Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2019 BASE Show details

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