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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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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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Abstract:
Experimental performance on the task of relation classification has generally improved using deep neural network architectures. One major drawback of reported studies is that individual models have been evaluated on a very narrow range of datasets, raising questions about the adaptability of the architectures, while making comparisons between approaches difficult. In this work, we present a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics. We propose a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. Our ‘Man for All Seasons’ approach achieves state-of-the-art performance on two datasets. More importantly, in our view, the model allowed us to obtain direct insights into the continued challenges faced by neural language models on this task. Example data and source code are available at: https://github.com/aidantee/ MASS. ; MRC
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URL: https://doi.org/10.17863/CAM.35331 https://www.repository.cam.ac.uk/handle/1810/288012
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