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1
Structural, Functional, and Processing Perspectives on Linguistic Island Effects
In: EISSN: 2333-9691 ; Annual Review of Linguistics ; https://hal.archives-ouvertes.fr/hal-03506510 ; Annual Review of Linguistics, Annual Reviews, 2022, 8 (1), ⟨10.1146/annurev-linguistics-011619-030319⟩ (2022)
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2
Universal Conditional Masked Language Pre-training for Neural Machine Translation ...
Li, Pengfei; Li, Liangyou; Zhang, Meng. - : arXiv, 2022
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3
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ...
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4
A Formal Model of Checked C ...
Li, Liyi; Liu, Yiyun; Postol, Deena L.. - : arXiv, 2022
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5
Entropy Analysis of Heart Rate Variability in Different Sleep Stages
In: Entropy; Volume 24; Issue 3; Pages: 379 (2022)
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6
A verb-frame frequency account of constraints on long-distance dependencies in English
In: Prof. Gibson (2022)
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7
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems ...
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8
Bridging Video-text Retrieval with Multiple Choice Questions ...
Ge, Yuying; Ge, Yixiao; Liu, Xihui. - : arXiv, 2022
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9
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking ...
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10
huggingface/datasets: 1.18.1 ...
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11
A Transposed-Word Effect in Chinese Reading ...
Paterson, Kevin; liu, zhiwei. - : University of Leicester, 2022
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12
A Transposed-Word Effect in Chinese Reading ...
Paterson, Kevin; liu, zhiwei. - : University of Leicester, 2022
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13
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition ...
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14
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information ...
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15
USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition ...
Abstract: This paper describes the system developed by the USTC-NELSLIP team for SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition (MultiCoNER). We propose a gazetteer-adapted integration network (GAIN) to improve the performance of language models for recognizing complex named entities. The method first adapts the representations of gazetteer networks to those of language models by minimizing the KL divergence between them. After adaptation, these two networks are then integrated for backend supervised named entity recognition (NER) training. The proposed method is applied to several state-of-the-art Transformer-based NER models with a gazetteer built from Wikidata, and shows great generalization ability across them. The final predictions are derived from an ensemble of these trained models. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on three tracks (Chinese, Code-mixed and Bangla) and 2nd on the ... : Winner system (USTC-NELSLIP) of SemEval 2022 MultiCoNER shared task on 3 tracks (Chinese, Bangla, Code-mixed) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2203.03216
https://dx.doi.org/10.48550/arxiv.2203.03216
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16
Implementation of multilingual support of the European Multicenter Study about Spinal Cord Injury (EMSCI) ISNCSCI calculator ...
Schuld, Christian; Franz, Steffen; Schweidler, Joachim. - : Nature Publishing Group, 2022
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17
Delving Deeper into Cross-lingual Visual Question Answering ...
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18
Zero-shot Cross-lingual Conversational Semantic Role Labeling ...
Wu, Han; Tan, Haochen; Xu, Kun. - : arXiv, 2022
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19
Multi-Level Contrastive Learning for Cross-Lingual Alignment ...
Chen, Beiduo; Guo, Wu; Gu, Bin. - : arXiv, 2022
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20
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition ...
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