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Structural, Functional, and Processing Perspectives on Linguistic Island Effects
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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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Universal Conditional Masked Language Pre-training for Neural Machine Translation ...
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Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ...
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Entropy Analysis of Heart Rate Variability in Different Sleep Stages
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In: Entropy; Volume 24; Issue 3; Pages: 379 (2022)
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A verb-frame frequency account of constraints on long-distance dependencies in English
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In: Prof. Gibson (2022)
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Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems ...
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Bridging Video-text Retrieval with Multiple Choice Questions ...
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The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking ...
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Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition ...
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Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information ...
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USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition ...
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Implementation of multilingual support of the European Multicenter Study about Spinal Cord Injury (EMSCI) ISNCSCI calculator ...
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Delving Deeper into Cross-lingual Visual Question Answering ...
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Zero-shot Cross-lingual Conversational Semantic Role Labeling ...
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Multi-Level Contrastive Learning for Cross-Lingual Alignment ...
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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 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2202.13083 https://arxiv.org/abs/2202.13083
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SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition ...
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