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A Quantitative and Qualitative Analysis of Schizophrenia Language ...
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Towards Responsible Natural Language Annotation for the Varieties of Arabic ...
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Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation ...
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AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization ...
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Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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Gender bias amplification during Speed-Quality optimization in Neural Machine Translation ...
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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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Discrete Cosine Transform as Universal Sentence Encoder ...
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Discrete Cosine Transform as Universal Sentence Encoder ...
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Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages ...
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Abstract:
We release an urgency dataset that consists of English tweets relating to natural crises. The set is annotated along with annotations of their corresponding urgency status. Additionally, we release evaluation datasets for two low-resource languages, i.e. Sinhala and Odia, and demonstrate an effective zero-shot transfer from English to these two languages by training cross-lingual classifiers. We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R. We train a variety of classifier architectures, supervised and semi supervised, on the extracted features. We also further experiment with ensembling the various classifiers. With very limited amounts of labeled data in English and zero data in the low resource languages, we show a successful framework of training monolingual and cross-lingual classifiers using deep learning methods which are known to be data hungry. Specifically, ...
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Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
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URL: https://underline.io/lecture/6152-detecting-urgency-status-of-crisis-tweets-a-transfer-learning-approach-for-low-resource-languages https://dx.doi.org/10.48448/13p8-4j86
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DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking ...
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Mutlitask Learning for Cross-Lingual Transfer of Semantic Dependencies ...
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Overview for the Second Shared Task on Language Identification in Code-Switched Data ...
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Creating a Large Multi-Layered Representational Repository of Linguistic Code Switched Arabic Data ...
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Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues ...
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Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task ...
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