{"id":4,"date":"2011-12-08T11:55:34","date_gmt":"2011-12-08T10:55:34","guid":{"rendered":"http:\/\/project.inria.fr\/template1\/?page_id=4"},"modified":"2020-10-07T11:47:13","modified_gmt":"2020-10-07T09:47:13","slug":"impress","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/impress\/","title":{"rendered":"IMPRESS"},"content":{"rendered":"

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Virtually all NLP systems nowadays use vector representations of words, a.k.a. word embeddings. Similarly, the processing of language combined with vision or other sensory modalities employs multimodal embeddings. While embeddings do embody some form of semantic relatedness, the exact nature of the latter remains unclear. This loss of precise semantic information can affect downstream tasks. Furthermore, while there is a growing body of NLP research on languages other than English, most research on multimodal embeddings is still done on English. The goals of IMPRESS are to investigate the integration of semantic knowledge into embeddings and its impact on selected downstream tasks, to extend this approach to multimodal and mildly multilingual settings, and to develop open source software and lexical resources, focusing on video activity recognition as a practical testbed.<\/p>\n

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Virtually all NLP systems nowadays use vector representations of words, a.k.a. word embeddings. Similarly, the processing of language combined with vision or other sensory modalities employs multimodal embeddings. While embeddings do embody some form of semantic relatedness, the exact nature of the latter remains unclear. This loss of precise semantic\u2026<\/p>\n

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