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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 – 10 of 10 1 Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input ... Lazaridou, Angeliki; Hermann, Karl Moritz; Tuyls, Karl. - : arXiv, 2018 BASE Show details 2 Grounded Language Learning in a Simulated 3D World ... Hermann, Karl Moritz; Hill, Felix; Green, Simon. - : arXiv, 2017 BASE Show details 3 Reasoning about Entailment with Neural Attention ... Rocktäschel, Tim; Grefenstette, Edward; Hermann, Karl Moritz. - : arXiv, 2015 BASE Show details 4 Learning Bilingual Word Representations by Marginalizing Alignments ... Kočiský, Tomáš; Hermann, Karl Moritz; Blunsom, Phil. - : arXiv, 2014 BASE Show details 5 Multilingual Models for Compositional Distributed Semantics ... Hermann, Karl Moritz; Blunsom, Phil. - : arXiv, 2014 BASE Show details 6 Deep Learning for Answer Sentence Selection ... Yu, Lei; Hermann, Karl Moritz; Blunsom, Phil. - : arXiv, 2014 BASE Show details 7 Distributed Representations for Compositional Semantics ... Hermann, Karl Moritz. - : arXiv, 2014 BASE Show details 8 Multilingual Distributed Representations without Word Alignment ... Hermann, Karl Moritz; Blunsom, Phil. - : arXiv, 2013 Abstract: Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP. By overcoming data sparsity problems, as well as providing information about semantic relatedness which is not available in discrete representations, distributed representations have proven useful in many NLP tasks. Recent work has shown how compositional semantic representations can successfully be applied to a number of monolingual applications such as sentiment analysis. At the same time, there has been some initial success in work on learning shared word-level representations across languages. We combine these two approaches by proposing a method for learning distributed representations in a multilingual setup. Our model learns to assign similar embeddings to aligned sentences and dissimilar ones to sentence which are not aligned while not requiring word alignments. We show that our representations are semantically informative and apply them to a cross-lingual document ... : To appear at ICLR 2014 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1312.6173 https://arxiv.org/abs/1312.6173 BASE Hide details 9 Parsing graphs with hyperedge replacement grammars Chiang, David; Hermann, Karl Moritz; Andreas, Jacob. - : Stroudsburg, PA : Association for Computational Linguistics, 2013 BASE Show details 10 Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input Lazaridou, Angeliki; Hermann, Karl Moritz; Tuyls, Karl. BASE Show details

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