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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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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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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 ...
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32 |
mSLAM: Massively multilingual joint pre-training for speech and text ...
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33 |
On the Representation Collapse of Sparse Mixture of Experts ...
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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 ...
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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 ...
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38 |
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers ...
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39 |
Factual Consistency of Multilingual Pretrained Language Models ...
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Abstract:
Pretrained language models can be queried for factual knowledge, with potential applications in knowledge base acquisition and tasks that require inference. However, for that, we need to know how reliable this knowledge is, and recent work has shown that monolingual English language models lack consistency when predicting factual knowledge, that is, they fill-in-the-blank differently for paraphrases describing the same fact. In this paper, we extend the analysis of consistency to a multilingual setting. We introduce a resource, mParaRel, and investigate (i) whether multilingual language models such as mBERT and XLM-R are more consistent than their monolingual counterparts; and (ii) if such models are equally consistent across languages. We find that mBERT is as inconsistent as English BERT in English paraphrases, but that both mBERT and XLM-R exhibit a high degree of inconsistency in English and even more so for all the other 45 languages. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2203.11552 https://dx.doi.org/10.48550/arxiv.2203.11552
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40 |
Examining Scaling and Transfer of Language Model Architectures for Machine Translation ...
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