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Statistical and Spatio-temporal Hand Gesture Features for Sign Language Recognition using the Leap Motion Sensor ...
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Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
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Zhang, Yi. - : Purdue University Graduate School, 2022
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Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
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Zhang, Yi. - : Purdue University Graduate School, 2022
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pNLP-Mixer: an Efficient all-MLP Architecture for Language ...
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Multilingual Abusiveness Identification on Code-Mixed Social Media Text ...
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hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification ...
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StableMoE: Stable Routing Strategy for Mixture of Experts ...
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BERTuit: Understanding Spanish language in Twitter through a native transformer ...
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EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification ...
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Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning ...
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Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages ...
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Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? ...
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Abstract:
Keyword extraction is the task of retrieving words that are essential to the content of a given document. Researchers proposed various approaches to tackle this problem. At the top-most level, approaches are divided into ones that require training - supervised and ones that do not - unsupervised. In this study, we are interested in settings, where for a language under investigation, no training data is available. More specifically, we explore whether pretrained multilingual language models can be employed for zero-shot cross-lingual keyword extraction on low-resource languages with limited or no available labeled training data and whether they outperform state-of-the-art unsupervised keyword extractors. The comparison is conducted on six news article datasets covering two high-resource languages, English and Russian, and four low-resource languages, Croatian, Estonian, Latvian, and Slovenian. We find that the pretrained models fine-tuned on a multilingual corpus covering languages that do not appear in the ...
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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/2202.06650 https://dx.doi.org/10.48550/arxiv.2202.06650
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Assessment of Massively Multilingual Sentiment Classifiers ...
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MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages ...
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DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition ...
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