A Multi-level Attention Model for Text Matching

Text matching based on deep learning models often suffer from the limitation of query term coverage problems. Inspired by the success of attention based models in machine translation, which the models can automatically search for parts of a sentence that are relevant to a target word, we propose a multi-level attention model with maximum matching matrix rank to simulate what human does when finding a good answer for a query question. Firstly, we apply a multi-attention mechanism to choose the high effect document words for every query words. Then an approach we called reciprocal relative standard deviation (RRSD) will calculate the matching coverage score for all query words. Experiments on both question-answer task and learning to rank task have achieved state-of-the-art results compared to traditional statistical methods and deep neural network methods.