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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 14 of 14 1 TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing ... Gui, Tao; Wang, Xiao; Zhang, Qi. - : arXiv, 2021 BASE Show details 2 SpanNER: Named Entity Re-/Recognition as Span Prediction ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Fu, Jinlan; Huang , Xuanjing. - : Underline Science Inc., 2021 BASE Show details 3 Align Voting Behavior with Public Statements for Legislator Representation Learning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chen , Lei; He , Yancheng. - : Underline Science Inc., 2021 BASE Show details 4 fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Geng, Zhichao; Huang , Xuanjing. - : Underline Science Inc., 2021 BASE Show details 5 {K-Adapter}: {I}nfusing {K}nowledge into {P}re-{T}rained {M}odels with {A}dapters ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cao, Guihong; Duan, Nan. - : Underline Science Inc., 2021 BASE Show details 6 Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chang, Kai-Wei; Hsieh, Cho-Jui. - : Underline Science Inc., 2021 BASE Show details 7 Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP Vaidhya, Tejas; Sachan, Mrinmaya; von Kügelgen, Julius... In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) BASE Show details 8 Classifying Dyads for Militarized Conflict Analysis Torroba Hennigen, Lucas; Stoehr, Niklas; West, Robert... In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) BASE Show details 9 Efficient Sampling of Dependency Structure Vieira, Tim; Zmigrod, Ran; Cotterell, Ryan In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) BASE Show details 10 Searching for More Efficient Dynamic Programs Cotterell, Ryan; Eisner, Jason; Vieira, Tim In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021) BASE Show details 11 “Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding Zhao, Chao; Sachan, Mrinmaya; Tafjord, Oyvind... In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021) BASE Show details 12 A Bayesian Framework for Information-Theoretic Probing Cotterell, Ryan; Pimentel, Tiago In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) Abstract: Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. The mutual information, however, assumes the true probability distribution of a pair of random variables is known, leading to unintuitive results in settings where it is not. This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents—allowing for more intuitive findings in scenarios with finite data. For instance, under Bayesian MI we have that data can add information, processing can help, and information can hurt, which makes it more intuitive for machine learning applications. Finally, we apply our framework to probing where we believe Bayesian mutual information naturally operationalises ease of extraction by explicitly limiting the available background knowledge to solve a task. URL: https://doi.org/10.3929/ethz-b-000518995 https://hdl.handle.net/20.500.11850/518995 BASE Hide details 13 Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation Manotumruksa, Jarana; Dalton, Jeffrey; Meij, Edgar. - 2021 BASE Show details 14 Come hither or go away? Recognising pre-electoral coalition signals in the news Rehbein, Ines; Ponzetto, Simone Paolo; Adendorf, Anna. - : Association for Computational Linguistics, 2021 BASE Show details

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