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Enhancing Cognitive Models of Emotions with Representation Learning ...
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Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation ...
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Abstract:
Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages. In this work, we propose a self-learning framework that further utilizes unlabeled data of target languages, combined with uncertainty estimation in the process to select high-quality silver labels. Three different uncertainties are adapted and analyzed specifically for the cross lingual transfer: Language Heteroscedastic/Homoscedastic Uncertainty (LEU/LOU), Evidential Uncertainty (EVI). We evaluate our framework with uncertainties on two cross-lingual tasks including Named Entity Recognition (NER) and Natural Language Inference (NLI) covering 40 languages in total, which outperforms the baselines significantly by 10 F1 on average for NER and 2.5 accuracy score for NLI. ... : Accepted to EMNLP 2021 ...
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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.2109.00194 https://arxiv.org/abs/2109.00194
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Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope ...
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The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders ...
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Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph ...
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Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering ...
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Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings ...
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Universal Dependencies 2.2
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In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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Universal Dependencies 2.1
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In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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