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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 Page: 1 2 3 4 5 Hits 1 – 20 of 95 1 Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences ... Duan, Xiangyu; Ji, Baijun; Jia, Hao. - : arXiv, 2020 BASE Show details 2 Explicit Reordering for Neural Machine Translation ... Chen, Kehai; Wang, Rui; Utiyama, Masao. - : arXiv, 2020 BASE Show details 3 Distributional Discrepancy: A Metric for Unconditional Text Generation ... Cai, Ping; Chen, Xingyuan; Jin, Peng. - : arXiv, 2020 BASE Show details 4 Query-Key Normalization for Transformers ... Henry, Alex; Dachapally, Prudhvi Raj; Pawar, Shubham. - : arXiv, 2020 BASE Show details 5 Iterative Domain-Repaired Back-Translation ... Wei, Hao-Ran; Zhang, Zhirui; Chen, Boxing. - : arXiv, 2020 BASE Show details 6 FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation ... Zhu, Wenhao; Huang, Shujian; Pu, Tong. - : arXiv, 2020 BASE Show details 7 Exemplar-Controllable Paraphrasing and Translation using Bitext ... Chen, Mingda; Wiseman, Sam; Gimpel, Kevin. - : arXiv, 2020 BASE Show details 8 Compiling ONNX Neural Network Models Using MLIR ... Jin, Tian; Bercea, Gheorghe-Teodor; Le, Tung D.. - : arXiv, 2020 BASE Show details 9 SQLFlow: A Bridge between SQL and Machine Learning ... Wang, Yi; Yang, Yang; Zhu, Weiguo. - : arXiv, 2020 BASE Show details 10 Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding ... Chen, Long; Ma, Wenbo; Xiao, Jun. - : arXiv, 2020 BASE Show details 11 SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check ... Cheng, Xingyi; Xu, Weidi; Chen, Kunlong. - : arXiv, 2020 BASE Show details 12 Cross-lingual Word Embeddings beyond Zero-shot Machine Translation ... Chen, Shifei; Basirat, Ali. - : arXiv, 2020 BASE Show details 13 Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation ... Siddhant, Aditya; Bapna, Ankur; Cao, Yuan. - : arXiv, 2020 BASE Show details 14 Synchronous Bidirectional Learning for Multilingual Lip Reading ... Luo, Mingshuang; Yang, Shuang; Chen, Xilin. - : arXiv, 2020 BASE Show details 15 Multilingual Translation with Extensible Multilingual Pretraining and Finetuning ... Tang, Yuqing; Tran, Chau; Li, Xian. - : arXiv, 2020 BASE Show details 16 GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding ... Lepikhin, Dmitry; Lee, HyoukJoong; Xu, Yuanzhong. - : arXiv, 2020 BASE Show details 17 Large-scale multilingual audio visual dubbing ... Yang, Yi; Shillingford, Brendan; Assael, Yannis. - : arXiv, 2020 BASE Show details 18 #Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election ... Chen, Emily; Deb, Ashok; Ferrara, Emilio. - : arXiv, 2020 BASE Show details 19 Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation ... Sun, Haipeng; Wang, Rui; Chen, Kehai. - : arXiv, 2020 BASE Show details 20 Model Selection for Cross-Lingual Transfer ... Chen, Yang; Ritter, Alan. - : arXiv, 2020 Abstract: Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities. In the zero-shot transfer setting, only English training data is used, and the fine-tuned model is evaluated on another target language. While this works surprisingly well, substantial variance has been observed in target language performance between different fine-tuning runs, and in the zero-shot setup, no target-language development data is available to select among multiple fine-tuned models. Prior work has relied on English dev data to select among models that are fine-tuned with different learning rates, number of steps and other hyperparameters, often resulting in suboptimal choices. In this paper, we show that it is possible to select consistently better models when small amounts of annotated data are available in auxiliary pivot languages. We propose a machine learning approach to model selection that uses the fine-tuned model's own internal ... : EMNLP 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/2010.06127 https://dx.doi.org/10.48550/arxiv.2010.06127 BASE Hide details Page: 1 2 3 4 5

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