Abstract

Learning sentence representation with semantic fulls of document is a challenge in natural language processing problems because if the semantic representation finding vector of the sentence is good, it will increase the performance of similar question problems. In this paper, we propose to implement a series of LSTM models with different ways of extracting sentence representations and apply them to question retrieval for the purpose of exploiting hidden semantics of sentences. T hese methods give sentence representation from hidden layers of the LSTM model. The results show that the technique using a combination of both Maxpooling and Meanpooling gives the highest results on the 2017 semeval dataset for the problem of finding similarity questions