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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 – 10 of 10 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; Zheng, Duo; Li, Zhixu; Qu, Jianfeng; Zhou, Jie. - : arXiv, 2022 Abstract: We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents from two subsets (i.e., SAMSum and MediaSum) and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART-50 (a multi-lingual BART) via further pre-training. The multiple objectives used in the further pre-training stage help the pre-trained model capture the structural characteristics as well as important content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2202.05599 https://dx.doi.org/10.48550/arxiv.2202.05599 BASE Hide 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. - : arXiv, 2021 BASE Show details 10 Competence-based Curriculum Learning for Multilingual Machine Translation ... Zhang, Mingliang; Meng, Fandong; Tong, Yunhai. - : arXiv, 2021 BASE Show details

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