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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; Chen, Yufeng; Zhou, Jie. - : arXiv, 2022 Abstract: Multimodal machine translation and textual chat translation have received considerable attention in recent years. Although the conversation in its natural form is usually multimodal, there still lacks work on multimodal machine translation in conversations. In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context. To this end, we firstly construct a Multimodal Sentiment Chat Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs in 14,762 bilingual dialogues and 30,370 English-German utterance pairs in 3,079 bilingual dialogues. Each utterance pair, corresponding to the visual context that reflects the current conversational scene, is annotated with a sentiment label. Then, we benchmark the task by establishing multiple baseline systems that incorporate multimodal and sentiment features for MCT. Preliminary experiments on four language directions ... : Accepted at ACL 2022 as a long paper of main conference. Code and data: https://github.com/XL2248/MSCTD ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2202.13645 https://arxiv.org/abs/2202.13645 BASE Hide 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. - : 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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