Research on Question Answering Technology Based on Bi- LSTM
Xiaoya Sun, Xinmeng Li · Journal of Physics Conference Series · 2019
Abstract Question Answering (QA) is always the key issue in the Natural Language Process (NLP). This paper mainly researches the question answering model based on Bi-directional Long Short Term Memory networks (Bi-LSTM), with the use of the WebQA dataset to train the model. Experiment results show that ACC@1 in Bi-LSTM model is 55.00%, ACC@3 is 73.24%, and ACC@10 reaches to 86.64%. The Bi-LSTM model outperforms Best Match 25 (BM25) algorithm in all of the three metrics, which proves the superiority of Bi-LSTM model.