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1
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang ...
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2
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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3
Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
Hou, Yifan; Sachan, Mrinmaya. - : arXiv, 2021
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4
Scaling Within Document Coreference to Long Texts ...
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5
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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6
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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7
Differentiable Subset Pruning of Transformer Heads ...
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8
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact ...
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9
How Good Is NLP?A Sober Look at NLP Tasks through the Lens of Social Impact ...
Jin, Zhijing; Chauhan, Geeticka; Tse, Brian. - : ETH Zurich, 2021
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10
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding ...
Brahman, Faeze; Huang, Meng; Tafjord, Oyvind. - : ETH Zurich, 2021
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11
Scaling Within Document Coreference to Long Texts ...
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12
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations ...
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13
Differentiable subset pruning of transformer heads ...
Li, Jiaoda; Cotterell, Ryan; Sachan, Mrinmaya. - : ETH Zurich, 2021
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14
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations ...
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15
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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16
How Good Is NLP?A Sober Look at NLP Tasks through the Lens of Social Impact
In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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17
Differentiable subset pruning of transformer heads
In: Transactions of the Association for Computational Linguistics, 9 (2021)
Abstract: Multi-head attention, a collection of several attention mechanisms that independently attend to different parts of the input, is the key ingredient in the Transformer. Recent work has shown, however, that a large proportion of the heads in a Transformer's multi-head attention mechanism can be safely pruned away without significantly harming the performance of the model; such pruning leads to models that are noticeably smaller and faster in practice. Our work introduces a new head pruning technique that we term differentiable subset pruning. Intuitively, our method learns per-head importance variables and then enforces a user-specified hard constraint on the number of unpruned heads. The importance variables are learned via stochastic gradient descent. We conduct experiments on natural language inference and machine translation; we show that differentiable subset pruning performs comparably or better than previous works while offering precise control of the sparsity level. ; ISSN:2307-387X
URL: https://doi.org/10.3929/ethz-b-000528141
https://hdl.handle.net/20.500.11850/528141
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18
Scaling Within Document Coreference to Long Texts
In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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19
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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20
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
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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