Abstract
Ad-hoc Retrieval based on deep learning model often suffers from the limitation of embedding semantic abuse problem. Inspired by the success of convolutional neural network based models in image processing, where a series of hidden layers extracts increasingly abstract features from a image, we propose a multidimensional interaction-focused model to solve the above problem in a image processing way. Firstly, we construct the query-document similarity matrix as a 3d tensor which means a word similarity value becomes a vector. Then we apply a CNN layer to capture complicated interaction patterns on every similarity chanel and a Bi-LSTM layer will map the output of CNN to a vector of fixed dimensionality. Finally a feed forward network will calculate a matching score. Experiments on the question-answer task with dataset WikiQA have achieved the state-of-the-art results compared to traditional statistical methods and deep neural network methods.
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Sun, Q., Wu, J., Wu, Y. (2018). A Multidimensional Interaction-Focused Model for Ad-Hoc Retrieval. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_23
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DOI: https://doi.org/10.1007/978-3-030-04179-3_23
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