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1
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice ...
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2
Improve Sentence Alignment by Divide-and-conquer ...
Zhang, Wu. - : arXiv, 2022
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3
A Neural Pairwise Ranking Model for Readability Assessment ...
Lee, Justin; Vajjala, Sowmya. - : arXiv, 2022
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4
SMDT: Selective Memory-Augmented Neural Document Translation ...
Zhang, Xu; Yang, Jian; Huang, Haoyang. - : arXiv, 2022
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5
Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks ...
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6
Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese ...
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7
Modeling Intensification for Sign Language Generation: A Computational Approach ...
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8
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition ...
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9
Open Source HamNoSys Parser for Multilingual Sign Language Encoding ...
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10
A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets ...
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11
Sign Language Video Retrieval with Free-Form Textual Queries ...
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12
pNLP-Mixer: an Efficient all-MLP Architecture for Language ...
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13
ParaNames: A Massively Multilingual Entity Name Corpus ...
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14
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments ...
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15
Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation ...
Chen, Liang; Xu, Runxin; Chang, Baobao. - : arXiv, 2022
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16
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information ...
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17
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation ...
Abstract: Multilingual neural machine translation models are trained to maximize the likelihood of a mix of examples drawn from multiple language pairs. The dominant inductive bias applied to these models is a shared vocabulary and a shared set of parameters across languages; the inputs and labels corresponding to examples drawn from different language pairs might still reside in distinct sub-spaces. In this paper, we introduce multilingual crossover encoder-decoder (mXEncDec) to fuse language pairs at an instance level. Our approach interpolates instances from different language pairs into joint `crossover examples' in order to encourage sharing input and output spaces across languages. To ensure better fusion of examples in multilingual settings, we propose several techniques to improve example interpolation across dissimilar languages under heavy data imbalance. Experiments on a large-scale WMT multilingual dataset demonstrate that our approach significantly improves quality on English-to-Many, Many-to-English and ... : ACL 2022 ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2203.07627
https://arxiv.org/abs/2203.07627
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18
IndicXNLI: Evaluating Multilingual Inference for Indian Languages ...
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19
MMTAfrica: Multilingual Machine Translation for African Languages ...
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20
mGPT: Few-Shot Learners Go Multilingual ...
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