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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 – 5 of 5 1 Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing ... Maudslay, Rowan Hall; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 2 Information-Theoretic Probing for Linguistic Structure ... Pimentel, Tiago; Valvoda, Josef; Maudslay, Rowan Hall. - : arXiv, 2020 BASE Show details 3 Pareto Probing: Trading Off Accuracy for Complexity ... Pimentel, Tiago; Saphra, Naomi; Williams, Adina. - : arXiv, 2020 BASE Show details 4 Combining Sentiment Lexica with a Multi-View Variational Autoencoder ... Hoyle, Alexander; Wolf-Sonkin, Lawrence; Wallach, Hanna. - : arXiv, 2019 BASE Show details 5 A Discriminative Latent-Variable Model for Bilingual Lexicon Induction ... Ruder, Sebastian; Cotterell, Ryan; Kementchedjhieva, Yova; Søgaard, Anders. - : arXiv, 2018 Abstract: We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical improvements on six language pairs under two metrics and show that the prior theoretically and empirically helps to mitigate the hubness problem. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior. ... : Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML URL: https://dx.doi.org/10.48550/arxiv.1808.09334 https://arxiv.org/abs/1808.09334 BASE Hide details

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