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1
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation ...
Lai, Siyu; Yang, Zhen; Meng, Fandong. - : arXiv, 2022
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2
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ...
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3
MSCTD: A Multimodal Sentiment Chat Translation Dataset ...
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4
ConSLT: A Token-level Contrastive Framework for Sign Language Translation ...
Fu, Biao; Ye, Peigen; Zhang, Liang. - : arXiv, 2022
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5
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation ...
Chen, Yutong; Wei, Fangyun; Sun, Xiao. - : arXiv, 2022
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6
Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation ...
Chen, Liang; Xu, Runxin; Chang, Baobao. - : arXiv, 2022
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7
USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition ...
Chen, Beiduo; Ma, Jun-Yu; Qi, Jiajun. - : arXiv, 2022
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8
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning ...
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9
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding ...
Qin, Libo; Chen, Qiguang; Xie, Tianbao. - : arXiv, 2022
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10
CINO: A Chinese Minority Pre-trained Language Model ...
Yang, Ziqing; Xu, Zihang; Cui, Yiming. - : arXiv, 2022
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11
Delving Deeper into Cross-lingual Visual Question Answering ...
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12
Multi-Level Contrastive Learning for Cross-Lingual Alignment ...
Abstract: Cross-language pre-trained models such as multilingual BERT (mBERT) have achieved significant performance in various cross-lingual downstream NLP tasks. This paper proposes a multi-level contrastive learning (ML-CTL) framework to further improve the cross-lingual ability of pre-trained models. The proposed method uses translated parallel data to encourage the model to generate similar semantic embeddings for different languages. However, unlike the sentence-level alignment used in most previous studies, in this paper, we explicitly integrate the word-level information of each pair of parallel sentences into contrastive learning. Moreover, cross-zero noise contrastive estimation (CZ-NCE) loss is proposed to alleviate the impact of the floating-point error in the training process with a small batch size. The proposed method significantly improves the cross-lingual transfer ability of our basic model (mBERT) and outperforms on multiple zero-shot cross-lingual downstream tasks compared to the same-size models in ... : Accepted by ICASSP 2022 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2202.13083
https://arxiv.org/abs/2202.13083
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13
Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training ...
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14
Taking an Emotional Look at Video Paragraph Captioning ...
Li, Qinyu; Li, Tengpeng; Wang, Hanli. - : arXiv, 2022
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15
HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity ...
Xu, Zihang; Yang, Ziqing; Cui, Yiming. - : arXiv, 2022
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16
Transforming Sequence Tagging Into A Seq2Seq Task ...
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17
Controllable Natural Language Generation with Contrastive Prefixes ...
Qian, Jing; Dong, Li; Shen, Yelong. - : arXiv, 2022
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18
SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following ...
Xu, Ruinian; Chen, Hongyi; Lin, Yunzhi. - : arXiv, 2022
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19
Local-Global Context Aware Transformer for Language-Guided Video Segmentation ...
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20
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset ...
Yu, Tiezheng; Frieske, Rita; Xu, Peng. - : arXiv, 2022
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