Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations. As real input noise is difficult to predict during training, robustness is a big issue for system deployment. In this paper, we improve the robustness of NMT models by reducing the effect of noisy words through a Context-Enhanced Reconstruction (CER) approach. CER trains the model to resist noise in two steps: (1) perturbation step that breaks the naturalness of input sequence with made-up words; (2) reconstruction step that defends the noise propagation by generating better and more robust contextual representation. Experimental results on Chinese-English (ZH-EN) and French-English (FR-EN) translation tasks demonstrate robustness improvement on both news and social media text. Further fine-tuning experiments on social media text show our approach can converge at a higher position and provide a better adaptation.
Nearest Neighbor Knowledge Distillation for Neural Machine Translation
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Center for Data Science, Peking University（北京大学数据科学中心）；The MOE Key Laboratory of Computational Linguistics, Peking University（北京大学计算语言学教育部重点实验室）
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation
Beihang University（北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing Jiaotong University（北京交通大学）；Beijing University of Technology（北京工业大学）；Institute of Data Space, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）
针对 NMT 对抗文本攻击性与隐蔽性不足的问题，本文提出 VFA 框架，通过视觉融合解空间增强和感知保留选择策略，在 LLaMA、GPT-3.5 等模型上显著提升 ASR 与 SSIM。
BhashaSetu: A Data-Centric Approach to Low-Resource Machine Translation
Department of Computer Engineering and Information Technology, Veermata Jijabai Technological Institute, Mumbai（孟买韦尔马塔·吉贾拜技术学院计算机工程与信息技术系）；Tübingen AI Center, University of Tübingen, Germany（图宾根大学图宾根人工智能中心，德国）
An Empirical Study on Chinese Character Decomposition in Multiword Expression-Aware Neural Machine Translation
Leiden University Medical Center（莱顿大学医学中心）；Leiden University（莱顿大学）；ADAPT Research Centre（ADAPT研究中心）；Dublin City University（都柏林城市大学）；Insight Centre for Data Analytics（洞察数据分析中心）
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda
Mbarara University of Science and Technology, Dept. of Information Technology（马拉拉大学科学与技术学院）；Handong Global University, Dept. of Computer Science and Electrical Engineering（汉东全球大学计算机科学与电气工程学院）
