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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 – 5 of 5 1 MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning ... Xia, Mengzhou; Zheng, Guoqing; Mukherjee, Subhabrata. - : arXiv, 2021 BASE Show details 2 Non-Parametric Few-Shot Learning for Word Sense Disambiguation ... Chen, Howard; Xia, Mengzhou; Chen, Danqi. - : arXiv, 2021 BASE Show details 3 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 4 Generalized Data Augmentation for Low-Resource Translation ... Xia, Mengzhou; Kong, Xiang; Anastasopoulos, Antonios. - : arXiv, 2019 BASE Show details 5 Domain Adaptation of Neural Machine Translation by Lexicon Induction ... Hu, Junjie; Xia, Mengzhou; Neubig, Graham; Carbonell, Jaime. - : arXiv, 2019 Abstract: It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift. In this paper, we argue that this is a dual effect of the highly lexicalized nature of NMT, resulting in failure for sentences with large numbers of unknown words, and lack of supervision for domain-specific words. To remedy this problem, we propose an unsupervised adaptation method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. Specifically, we perform lexicon induction to extract an in-domain lexicon, and construct a pseudo-parallel in-domain corpus by performing word-for-word back-translation of monolingual in-domain target sentences. In five domains over twenty pairwise adaptation settings and two model architectures, our method achieves consistent improvements without using any in-domain parallel sentences, improving up to 14 BLEU over unadapted models, and up to 2 BLEU over strong back-translation baselines. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1906.00376 https://dx.doi.org/10.48550/arxiv.1906.00376 BASE Hide details

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