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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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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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Abstract:
Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. The biases make the learned models unfair and can even exacerbate the marginalization of people. Considering that current debiasing methods for general natural language understanding tasks cannot effectively mitigate the biases in the toxicity detectors, we propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns (e.g., identity mentions, dialect) to toxicity labels. We empirically show that our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods. ... : The 5th Workshop on Online Abuse and Harms at ACL 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2106.07240 https://dx.doi.org/10.48550/arxiv.2106.07240
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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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