A Hybrid and Regenerative Model Chat Robot Based on LSTM and Attention Model

EasyChair Preprint 5236

Abstract

Aiming at the situation that retrieval chat robot relies too much on predefined responses and the training requirements of generative chat robot are too high, a hybrid and regenerative model text chat robot based on LSTM and Attention-model is designed. Due to the retrieval model can only handle scenarios with predefined responses, and a generative model with strong learning ability will produce grammatical errors in certain scenarios. Therefore, firstly,doing text processing based on corpus, and then the retrieval model generates a candidate data set, and  the candidate data set is trained by generating model to obtain the final model. The experimental comparison results show that the hybrid and regenerative model chat robot can effectively improve the model response quality compared to the single model chat robot, and accuracy improved by thirty percent.

Keyphrases: Attention Model, NLP, Seq2Seq, chat robot, deep learning, text generation

@booklet{EasyChair:5236,
  author    = {Dongyang Gao and Junwu Zhu and Fudong Li},
  title     = {A Hybrid and Regenerative Model Chat Robot Based on LSTM and Attention Model},
  howpublished = {EasyChair Preprint 5236},
  year      = {EasyChair, 2021}}