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Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models ...
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Improving Cross-Lingual Reading Comprehension with Self-Training ...
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Abstract:
Substantial improvements have been made in machine reading comprehension, where the machine answers questions based on a given context. Current state-of-the-art models even surpass human performance on several benchmarks. However, their abilities in the cross-lingual scenario are still to be explored. Previous works have revealed the abilities of pre-trained multilingual models for zero-shot cross-lingual reading comprehension. In this paper, we further utilized unlabeled data to improve the performance. The model is first supervised-trained on source language corpus, and then self-trained with unlabeled target language data. The experiment results showed improvements for all languages, and we also analyzed how self-training benefits cross-lingual reading comprehension in qualitative aspects. ... : 8 pages, 4 figures ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2105.03627 https://dx.doi.org/10.48550/arxiv.2105.03627
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Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation ...
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S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations ...
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Mitigating Biases in Toxic Language Detection through Invariant Rationalization ...
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Mitigating Biases in Toxic Language Detection through Invariant Rationalization ...
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Looking for Clues of Language in Multilingual BERT to Improve Cross-lingual Generalization ...
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DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation ...
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A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT ...
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Pretrained Language Model Embryology: The Birth of ALBERT ...
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AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalization ...
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VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecture ...
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Defending Your Voice: Adversarial Attack on Voice Conversion ...
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FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention ...
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Training a code-switching language model with monolingual data ...
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Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model ...
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Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning ...
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From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings ...
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Improved Speech Separation with Time-and-Frequency Cross-domain Joint Embedding and Clustering ...
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