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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 – 18 of 18 1 Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation ... Lai, Siyu; Yang, Zhen; Meng, Fandong. - : arXiv, 2022 BASE Show details 2 Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ... Zhang, Songming; Liu, Yijin; Meng, Fandong. - : arXiv, 2022 BASE Show details 3 MSCTD: A Multimodal Sentiment Chat Translation Dataset ... Liang, Yunlong; Meng, Fandong; Xu, Jinan. - : arXiv, 2022 BASE Show details 4 ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization ... Wang, Jiaan; Meng, Fandong; Lu, Ziyao. - : arXiv, 2022 BASE Show details 5 EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation ... Xu, Yulin; Yang, Zhen; Meng, Fandong. - : arXiv, 2022 BASE Show details 6 A Survey on Cross-Lingual Summarization ... Wang, Jiaan; Meng, Fandong; Zheng, Duo. - : arXiv, 2022 BASE Show details 7 Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ... Xu, Yangyifan; Liu, Yijin; Meng, Fandong. - : arXiv, 2021 BASE Show details 8 Modeling Bilingual Conversational Characteristics for Neural Chat Translation ... Liang, Yunlong; Meng, Fandong; Chen, Yufeng. - : arXiv, 2021 BASE Show details 9 Sequence-Level Training for Non-Autoregressive Neural Machine Translation ... Shao, Chenze; Feng, Yang; Zhang, Jinchao; Meng, Fandong; Zhou, Jie. - : arXiv, 2021 Abstract: In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregressive mechanism leads to high translation latency of the NMT and restricts its low-latency applications. Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup through generating target words independently and simultaneously. Nevertheless, NAT still takes the word-level cross-entropy loss as the training objective, which is not optimal because the output of NAT cannot be properly evaluated due to the multimodality problem. In this article, we propose using sequence-level training objectives to train NAT models, which evaluate the NAT outputs as a whole and correlates well with the real translation quality. Firstly, we propose training NAT models to optimize sequence-level evaluation metrics (e.g., BLEU) based on several novel reinforcement algorithms ... : Computational Linguistics Journal ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; Machine Learning cs.LG URL: https://arxiv.org/abs/2106.08122 https://dx.doi.org/10.48550/arxiv.2106.08122 BASE Hide details 10 Competence-based Curriculum Learning for Multilingual Machine Translation ... Zhang, Mingliang; Meng, Fandong; Tong, Yunhai. - : arXiv, 2021 BASE Show details 11 Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Fandong; Lin, Zheng. - : Underline Science Inc., 2021 BASE Show details 12 Towards Making the Most of Dialogue Characteristics for Neural Chat Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Fandong; Chen, Yufeng. - : Underline Science Inc., 2021 BASE Show details 13 Prevent the Language Model from being Overconfident in Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Fandong; Liu, Yijin. - : Underline Science Inc., 2021 BASE Show details 14 Modeling Bilingual Conversational Characteristics for Neural Chat Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Fandong; Chen, Yufeng. - : Underline Science Inc., 2021 BASE Show details 15 Selective Knowledge Distillation for Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Fandong; Wang, Fusheng. - : Underline Science Inc., 2021 BASE Show details 16 Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Fandong; Chen, Yufeng. - : Underline Science Inc., 2021 BASE Show details 17 Exploring Dynamic Selection of Branch Expansion Orders for Code Generation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Fandong; Huang, Degen. - : Underline Science Inc., 2021 BASE Show details 18 The ICT ’s Patent MT System Description for NTCIR-9 Fandong Meng; Hao Xiong; Yajuan Lü... In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings9/NTCIR/19-NTCIR9-PATENTMT-XiongH.pdf BASE Show details

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