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Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact ...
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How Good Is NLP?A Sober Look at NLP Tasks through the Lens of Social Impact ...
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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding ...
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Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations ...
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Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations ...
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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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How Good Is NLP?A Sober Look at NLP Tasks through the Lens of Social Impact
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In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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Differentiable subset pruning of transformer heads
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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Scaling Within Document Coreference to Long Texts
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In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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Abstract:
State of the art end-to-end coreference resolution models use expensive span representations and antecedent prediction mechanisms. These approaches are expensive both in terms of their memory requirements as well as compute time, and are particularly ill-suited for long documents. In this paper, we propose an approximation to end-to-end models which scales gracefully to documents of any length. Replacing span representations with token representations, we reduce the time/memory complexity via token windows and nearest neighbor sparsification methods for more efficient antecedent prediction. We show our approach’s resulting reduction of training and inference time compared to state-of-the-art methods with only a minimal loss in accuracy.
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URL: https://doi.org/10.3929/ethz-b-000527310 https://hdl.handle.net/20.500.11850/527310
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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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