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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 Page: 1 2 Hits 1 – 20 of 33 1 Finding Structural Knowledge in Multimodal-BERT ... Milewski, Victor; de Lhoneux, Miryam; Moens, Marie-Francine. - : arXiv, 2022 BASE Show details 2 Modeling Coreference Relations in Visual Dialog ... Li, Mingxiao; Moens, Marie-Francine. - : arXiv, 2022 BASE Show details 3 Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP Vaidhya, Tejas; Sachan, Mrinmaya; von Kügelgen, Julius; Kaushal, Ayush; Schölkopf, Bernhard; Ni, Jingwei; Jin, Zhijing In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) Abstract: The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other. While this idea has led to fruitful developments in the field of causal inference, it is not widely-known in the NLP community. In this work, we argue that the causal direction of the data collection process bears nontrivial implications that can explain a number of published NLP findings, such as differences in semi-supervised learning (SSL) and domain adaptation (DA) performance across different settings. We categorize common NLP tasks according to their causal direction and empirically assay the validity of the ICM principle for text data using minimum description length. We conduct an extensive meta-analysis of over 100 published SSL and 30 DA studies, and find that the results are consistent with our expectations based on causal insights. This work presents the first attempt to analyze the ICM principle in NLP, and provides constructive suggestions for future modeling choices. URL: https://doi.org/10.3929/ethz-b-000527298 https://hdl.handle.net/20.500.11850/527298 BASE Hide details 4 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 5 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 6 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 7 “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 8 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 9 Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation Manotumruksa, Jarana; Dalton, Jeffrey; Meij, Edgar. - 2021 BASE Show details 10 AWESSOME : An unsupervised sentiment intensity scoring framework using neural word embeddings Htait, Amal; Azzopardi, Leif. - : Springer, 2021 BASE Show details 11 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 12 Evaluating multilingual text encoders for unsupervised cross-lingual retrieval Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer, 2021 BASE Show details 13 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 14 LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification ... Ghadery, Erfan; Moens, Marie-Francine. - : arXiv, 2020 BASE Show details 15 Autoregressive Reasoning over Chains of Facts with Transformers ... Cartuyvels, Ruben; Spinks, Graham; Moens, Marie-Francine. - : arXiv, 2020 BASE Show details 16 Rethinking summarization and storytelling for modern social multimedia Rudinac, Stevan; Chua, Tat-Seng; Diaz-Ferreyra, Nicolas... 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) BASE Show details 17 Word-Level Loss Extensions for Neural Temporal Relation Classification ... Leeuwenberg, Artuur; Moens, Marie-Francine. - : arXiv, 2018 BASE Show details 18 A deep learning approach to bilingual lexicon induction in the biomedical domain ... Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 19 A deep learning approach to bilingual lexicon induction in the biomedical domain. ... Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 20 A deep learning approach to bilingual lexicon induction in the biomedical domain. Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018 BASE Show details Page: 1 2

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