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Improving Word Translation via Two-Stage Contrastive Learning ...
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Plan-then-Generate: Controlled Data-to-Text Generation via Planning ...
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Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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Visually Grounded Reasoning across Languages and Cultures ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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Visually Grounded Reasoning across Languages and Cultures ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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Self-Alignment Pretraining for Biomedical Entity Representations
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Liu, Fangyu; Shareghi, Ehsan; Meng, Zaiqiao. - : Association for Computational Linguistics, 2021. : Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021
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Large-scale exploration of neural relation classification architectures ...
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Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter ...
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Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter
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Abstract:
We present a new challenging stance detection dataset, called Will-They-Won’t-They (WT--WT), which contains 51,284 tweets in English, making it by far the largest available dataset of the type. All the annotations are carried out by experts; therefore, the dataset constitutes a high-quality and reliable benchmark for future research in stance detection. Our experiments with a wide range of recent state-of-the-art stance detection systems show that the dataset poses a strong challenge to existing models in this domain. ; Keynes Fund, Cambridge
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URL: https://doi.org/10.17863/CAM.55294 https://www.repository.cam.ac.uk/handle/1810/308201
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STANDER: An expert-annotated dataset for news stance detection and evidence retrieval
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Conforti, C; Berndt, J; Pilehvar, MT. - : Association for Computational Linguistics, 2020. : Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020, 2020
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Large-scale exploration of neural relation classification architectures
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Le, HQ; Can, DC; Vu, ST. - : Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 2020
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