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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 Page: 1 2 3 4 Hits 1 – 20 of 73 1 AUTOLEX: An Automatic Framework for Linguistic Exploration ... Chaudhary, Aditi; Sheikh, Zaid; Mortensen, David R. - : arXiv, 2022 BASE Show details 2 MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages ... Wang, Zhiruo; Cuenca, Grace; Zhou, Shuyan. - : arXiv, 2022 BASE Show details 3 A Systematic Evaluation of Large Language Models of Code ... Xu, Frank F.; Alon, Uri; Neubig, Graham. - : arXiv, 2022 BASE Show details 4 Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation ... Wang, Xinyi; Ruder, Sebastian; Neubig, Graham. - : arXiv, 2022 BASE Show details 5 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 6 Few-shot Language Coordination by Modeling Theory of Mind ... Zhu, Hao; Neubig, Graham; Bisk, Yonatan. - : arXiv, 2021 BASE Show details 7 Systematic Inequalities in Language Technology Performance across the World's Languages ... Blasi, Damián; Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2021 BASE Show details 8 Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ... Huang, Po-Yao; Patrick, Mandela; Hu, Junjie. - : arXiv, 2021 BASE Show details 9 Multi-view Subword Regularization ... Wang, Xinyi; Ruder, Sebastian; Neubig, Graham. - : arXiv, 2021 BASE Show details 10 MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning ... Xia, Mengzhou; Zheng, Guoqing; Mukherjee, Subhabrata. - : arXiv, 2021 BASE Show details 11 XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ... Ruder, Sebastian; Constant, Noah; Botha, Jan. - : arXiv, 2021 BASE Show details 12 When Does Translation Require Context? A Data-driven, Multilingual Exploration ... Yin, Kayo; Fernandes, Patrick; Martins, André F. T.. - : arXiv, 2021 BASE Show details 13 Breaking Down Multilingual Machine Translation ... Chiang, Ting-Rui; Chen, Yi-Pei; Yeh, Yi-Ting. - : arXiv, 2021 BASE Show details 14 Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ... Wang, Xinyi; Tsvetkov, Yulia; Ruder, Sebastian. - : arXiv, 2021 BASE Show details 15 Distributionally Robust Multilingual Machine Translation ... Zhou, Chunting; Levy, Daniel; Li, Xian. - : arXiv, 2021 BASE Show details 16 AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages ... Reid, Machel; Hu, Junjie; Neubig, Graham. - : arXiv, 2021 BASE Show details 17 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... Pratapa, Adithya; Anastasopoulos, Antonios; Rijhwani, Shruti. - : arXiv, 2021 BASE Show details 18 Meta Back-translation ... Pham, Hieu; Wang, Xinyi; Yang, Yiming. - : arXiv, 2021 BASE Show details 19 On The Ingredients of an Effective Zero-shot Semantic Parser ... Yin, Pengcheng; Wieting, John; Sil, Avirup; Neubig, Graham. - : arXiv, 2021 Abstract: Semantic parsers map natural language utterances into meaning representations (e.g., programs). Such models are typically bottlenecked by the paucity of training data due to the required laborious annotation efforts. Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterances to improve linguistic diversity. However, such synthetic examples cannot fully capture patterns in real data. In this paper we analyze zero-shot parsers through the lenses of the language and logical gaps (Herzig and Berant, 2019), which quantify the discrepancy of language and programmatic patterns between the canonical examples and real-world user-issued ones. We propose bridging these gaps using improved grammars, stronger paraphrasers, and efficient learning methods using canonical examples that most likely reflect real user intents. Our model achieves strong performance on two semantic parsing benchmarks (Scholar, ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2110.08381 https://arxiv.org/abs/2110.08381 BASE Hide details 20 DEEP: DEnoising Entity Pre-training for Neural Machine Translation ... Hu, Junjie; Hayashi, Hiroaki; Cho, Kyunghyun. - : arXiv, 2021 BASE Show details Page: 1 2 3 4

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