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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 – 8 of 8 1 Dialogue in the Wild: Learning from a Deployed Role-Playing Game with Humans and Bots ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Dinan, Emily; Shuster, Kurt. - : Underline Science Inc., 2021 BASE Show details 2 I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bansal, Mohit; Kiela, Douwe. - : Underline Science Inc., 2021 BASE Show details 3 Emergent Translation in Multi-Agent Communication ... Lee, Jason; Cho, Kyunghyun; Weston, Jason. - : arXiv, 2017 BASE Show details 4 Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution Rush, Alexander Matthew; Weston, Jason; Wiseman, Sam Joshua. - : Association for Computational Linguistics, 2015 BASE Show details 5 Open Question Answering with Weakly Supervised Embedding Models Bordes, Antoine; Weston, Jason; Usunier, Nicolas In: European Conference (ECML PKDD 2014) ; https://hal.archives-ouvertes.fr/hal-01344007 ; European Conference (ECML PKDD 2014), Sep 2014, nancy, France. pp.165-180, ⟨10.1007/978-3-662-44848-9_11⟩ (2014) Abstract: International audience ; Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over paralex, the only existing method able to be trained on similar weakly labeled data. Keyword: [INFO]Computer Science [cs] URL: https://hal.archives-ouvertes.fr/hal-01344007 https://doi.org/10.1007/978-3-662-44848-9_11 BASE Hide details 6 Open Question Answering with Weakly Supervised Embedding Models ... Bordes, Antoine; Weston, Jason; Usunier, Nicolas. - : arXiv, 2014 BASE Show details 7 Towards Understanding Situated Natural Language Bordes, Antoine; Usunier, Nicolas; Collobert, Ronan... In: 13th International Conference on Artificial Intelligence and Statistics ; https://hal.archives-ouvertes.fr/hal-00750937 ; 13th International Conference on Artificial Intelligence and Statistics, May 2010, Chia Laguna Resort, Sardinia, Italy. pp.65-72 (2010) BASE Show details 8 Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts Barnickel, Thorsten; Weston, Jason; Collobert, Ronan. - : Public Library of Science, 2009 BASE Show details

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