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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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Classifying Dyads for Militarized Conflict Analysis
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Efficient Sampling of Dependency Structure
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Searching for More Efficient Dynamic Programs
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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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.
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URL: https://hdl.handle.net/20.500.11850/518987 https://doi.org/10.3929/ethz-b-000518987
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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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A Bayesian Framework for Information-Theoretic Probing
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation
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AWESSOME : An unsupervised sentiment intensity scoring framework using neural word embeddings
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Robust fragment-based framework for cross-lingual sentence retrieval
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 ; 935 ; 944 (2021)
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Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
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Come hither or go away? Recognising pre-electoral coalition signals in the news
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LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification ...
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Autoregressive Reasoning over Chains of Facts with Transformers ...
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Rethinking summarization and storytelling for modern social multimedia
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In: Rudinac, Stevan, Chua, Tat-Seng, Diaz-Ferreyra, Nicolas, Friedland, Gerald, Gornostaja, Tatjana, Huet, Benoit, Kaptein, Rianne, Lindén, Krister, Moens, Marie-Francine, Peltonen, Jaakko, Redi, Miriam, Schedl, Markus, Shamma, David A, Smeaton, Alan F. orcid:0000-0003-1028-8389 and Xie, Lexing (2018) Rethinking summarization and storytelling for modern social multimedia. In: The 24th International Conference on Multimedia Modeling (MMM2018), 5-7 Feb, 2018, Bangkok, Thailand. ISBN 978-3-319-73599-3 (2018)
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Word-Level Loss Extensions for Neural Temporal Relation Classification ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain.
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