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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 – 9 of 9 1 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 2 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 3 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 4 Searching for More Efficient Dynamic Programs Cotterell, Ryan; Eisner, Jason; Vieira, Tim In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021) Abstract: Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a heuristic search procedure to improve this metric. We show that in practice, automated search—like the mental search performed by human programmers—can find substantial improvements to the initial program. Empirically, we show that many speed-ups described in the NLP literature could have been discovered automatically by our system. URL: https://hdl.handle.net/20.500.11850/518987 https://doi.org/10.3929/ethz-b-000518987 BASE Hide details 5 “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 6 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) BASE Show details 7 Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation Manotumruksa, Jarana; Dalton, Jeffrey; Meij, Edgar. - 2021 BASE Show details 8 Robust fragment-based framework for cross-lingual sentence retrieval Nutanong, Sarana; Sarwar, Raheem; Phatthiyaphaibun, Wannaphong... In: Findings of the Association for Computational Linguistics: EMNLP 2021 ; 935 ; 944 (2021) BASE Show details 9 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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