A Joint Model for Sentence Semantic Similarity Learning

Sentence similarity is a fundamental task to measure the degree of the likelihood between two sentences. It plays an important role in both NLP and IR communities. The current sentence semantic similarity measurements can be divided into two categories: the model based on feature engineering and the model based on deep learning. These two categories all have their own advantages and disadvantages. The feature captured based on deep learning may not get the deep meaning of the sentence. And the feature exacted based on feature engineering may not be comprehensive. The algorithm proposed recently usually use one of these two models. In this paper, we proposed a new model that combine the deep learning model and the feature engineering model together. Our model enables two methods to complement each other, making the feature extraction more comprehensive: not only take the global semantic information into account, but also further refine the semantic information at the word level. The experiment shows that our model performs better than models only use one category.