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Learning Neural Representation for CLIR with Adversarial Framework
In this paper, we follow the success of neural representation in natural language processing (NLP) and develop a novel text representation model based on adversarial learning, which seeks a task-specific embedding space for CLIR.
Joint Learning from Labeled and Unlabeled Data for Information Retrieval
To this end, we propose a learning framework which can benefit from both labeled and more abundant unlabeled data for representation learning in the context of IR.
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