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A Neural Pairwise Ranking Model for Readability Assessment ...
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Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition ...
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A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets ...
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Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition ...
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Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns ...
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WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language ...
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A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition ...
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Including Facial Expressions in Contextual Embeddings for Sign Language Generation ...
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Statistical and Spatio-temporal Hand Gesture Features for Sign Language Recognition using the Leap Motion Sensor ...
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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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Assessment of Massively Multilingual Sentiment Classifiers ...
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Abstract:
Models are increasing in size and complexity in the hunt for SOTA. But what if those 2\% increase in performance does not make a difference in a production use case? Maybe benefits from a smaller, faster model outweigh those slight performance gains. Also, equally good performance across languages in multilingual tasks is more important than SOTA results on a single one. We present the biggest, unified, multilingual collection of sentiment analysis datasets. We use these to assess 11 models and 80 high-quality sentiment datasets (out of 342 raw datasets collected) in 27 languages and included results on the internally annotated datasets. We deeply evaluate multiple setups, including fine-tuning transformer-based models for measuring performance. We compare results in numerous dimensions addressing the imbalance in both languages coverage and dataset sizes. Finally, we present some best practices for working with such a massive collection of datasets and models from a multilingual perspective. ... : Accepted for WASSA at ACL 2022 ...
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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://dx.doi.org/10.48550/arxiv.2204.04937 https://arxiv.org/abs/2204.04937
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