| Auteur | Rechercher : Zhu, Xiaodan1; Rechercher : Sobhani, Parinaz; Rechercher : Guo, Hongyu1 |
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| Affiliation du nom | - Conseil national de recherches du Canada. Technologies de l'information et des communications
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| Format | Texte, Article |
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| Conférence | 32nd International Conference on Machine Learning, July 6-11, 2015, Lille, France |
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| Sujet | artificial intelligence; brain; computational linguistics; semantics; speech recognition; speech transmission; trees (mathematics); composition layers; long distance interactions; long short term memory; machine translations; natural language understanding; recursive modeling; recursive structure; semantic composition; learning systems |
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| Résumé | The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we propose to extend it to tree structures, in which a memory cell can reflect the history memories of multiple child cells or multiple descendant cells in a recursive process. We call the model S-LSTM, which provides a principled way of considering long-distance interaction over hierarchies, e.g., language or image parse structures. We leverage the models for semantic composition to understand the meaning of text, a fundamental problem in natural language understanding, and show that it outperforms a state-of-the-art recursive model by replacing its composition layers with the S-LSTM memory blocks. We also show that utilizing the given structures is helpful in achieving a performance better than that without considering the structures. |
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| Date de publication | 2016-03 |
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| Maison d’édition | International Machine Learning Society |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 23000279 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 3f52b3f9-330f-4ca8-95b8-1c3a68fc4fa7 |
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| Enregistrement créé | 2016-07-04 |
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| Enregistrement modifié | 2020-03-16 |
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