| Summary: | Abstract In this paper, we propose a novel relevance evaluation method using labels collected from crowdsourcing. The proposed method not only predicts the relevance between query texts and responses in information retrieval systems but also performs the label aggregation tasks simultaneously. It first merges two kinds of heterogeneous data (i.e., image and query text) and constructs a CNN-like deep neural network. Then, on the top of its softmax layer, an additional layer was built to model the crowd workers. Finally, classification models for relevance prediction and aggregated labels for training examples can be simultaneously learned from noisy labels. Experimental results show that the proposed method significantly outperforms other state-of-the-art methods on a real-world dataset.
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