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21
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? ...
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22
Assessment of Massively Multilingual Sentiment Classifiers ...
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23
Multilingual and Multimodal Abuse Detection ...
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24
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages ...
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25
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition ...
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26
DeepNet: Scaling Transformers to 1,000 Layers ...
Wang, Hongyu; Ma, Shuming; Dong, Li. - : arXiv, 2022
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27
Adapting BigScience Multilingual Model to Unseen Languages ...
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28
On Efficiently Acquiring Annotations for Multilingual Models ...
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29
Team ÚFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models ...
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30
Does Corpus Quality Really Matter for Low-Resource Languages? ...
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31
IIITDWD-ShankarB@ Dravidian-CodeMixi-HASOC2021: mBERT based model for identification of offensive content in south Indian languages ...
Biradar, Shankar; Saumya, Sunil. - : arXiv, 2022
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32
mSLAM: Massively multilingual joint pre-training for speech and text ...
Bapna, Ankur; Cherry, Colin; Zhang, Yu. - : arXiv, 2022
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33
On the Representation Collapse of Sparse Mixture of Experts ...
Chi, Zewen; Dong, Li; Huang, Shaohan. - : arXiv, 2022
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34
Politics and Virality in the Time of Twitter: A Large-Scale Cross-Party Sentiment Analysis in Greece, Spain and United Kingdom ...
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35
L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models ...
Abstract: Social media platforms are used by a large number of people prominently to express their thoughts and opinions. However, these platforms have contributed to a substantial amount of hateful and abusive content as well. Therefore, it is important to curb the spread of hate speech on these platforms. In India, Marathi is one of the most popular languages used by a wide audience. In this work, we present L3Cube-MahaHate, the first major Hate Speech Dataset in Marathi. The dataset is curated from Twitter, annotated manually. Our dataset consists of over 25000 distinct tweets labeled into four major classes i.e hate, offensive, profane, and not. We present the approaches used for collecting and annotating the data and the challenges faced during the process. Finally, we present baseline classification results using deep learning models based on CNN, LSTM, and Transformers. We explore mono-lingual and multi-lingual variants of BERT like MahaBERT, IndicBERT, mBERT, and xlm-RoBERTa and show that mono-lingual models ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.2203.13778
https://arxiv.org/abs/2203.13778
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36
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts ...
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37
A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model ...
Sun, Xin; Ge, Tao; Ma, Shuming. - : arXiv, 2022
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38
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers ...
Lees, Alyssa; Tran, Vinh Q.; Tay, Yi. - : arXiv, 2022
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39
Factual Consistency of Multilingual Pretrained Language Models ...
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40
Examining Scaling and Transfer of Language Model Architectures for Machine Translation ...
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