Neural Generative Question Answering

Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, Xiaoming Li

Abstract: This paper presents an end-to-end neural network model, named Neural Generative Question Answering (genQA), that can generate answers to simple factoid questions, both in natural language. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to access an embedded knowledge-base through an attention-like mechanism. The model is trained on a corpus of question-answer pairs, with their associated triples in the given knowledge-base. Empirical study shows the proposed model can effectively deal with the language variation of the question and generate a right answer by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform the embedding-based QA model as well as the neural dialogue models trained on the same data.

Conflicts: huawei.com, pku.edu.cn