Deep Paraphrase Detection in Indian Languages

Abstract

This paper presents an approach to the problem of paraphrase identification in English and Indian languages using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional machine learning approaches used features that involved using resources such as POS taggers, dependency parsers, etc. for English. The lack of similar resources for Indian languages has been a deterrent to the advancement of paraphrase detection task in Indian languages. Deep learning helps in overcoming the shortcomings of traditional machine Learning techniques. In this paper, three approaches have been proposed, a simple CNN that uses word embeddings as input, a CNN that uses WordNet scores as input and RNN based approach with both LSTM and bi-directional LSTM.