Duplicate Questions Pair Detection Using Siamese MaLSTM

Quora is a growing platform comprising a user generated collection of questions and answers. The questions and answers are created, edited, and organized by the users. Enormous number of users on the Quora website makes it unavoidable to have multiple questions from different users with similar intent, which raises the issue of duplicate questions. Effectively detecting duplicate questions would make it easier to find high quality answers and help save time, which in turn would result in an improved user experience for writers and readers on Quora. In this paper, Quora Question Pairs dataset is collected from Kaggle for detection of duplicate questions. First, three types of word embeddings involving Google news vector embedding, FastText crawl embedding with 300 dimensions, and FastText crawl sub words embedding with 300 dimensions are implemented individually to vectorize all the questions and train the model. The final features used for prediction are blend of these three types of word embeddings. Then, Siamese MaLSTM ('Ma' for Manhattan distance) Neural Network model is applied for prediction of duplicate questions in the dataset. Finally, the model is tested on 100000 pairs of questions. The experiments show that the proposed model achieves 91.14% accuracy which is better than the state-of-the-art models.