2016 NeuralGenerativeQuestionAnsweri
- (Yin et al., 2016) ⇒ Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, and Xiaoming Li. (2016). “Neural Generative Question Answering.” In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). ISBN:978-1-57735-770-4. arXiv preprint arXiv:1512.01337.
Notes
Cited By
- http://scholar.google.com/scholar?q=%222016%22+Neural+Generative+Question+Answering
- http://dl.acm.org/citation.cfm?id=3060832.3061037&preflayout=flat#citedby
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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, based on the facts in a knowledge-base. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to enquire the knowledge-base, and is trained on a corpus of question-answer pairs, with their associated triples in the knowledge-base. Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data.
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Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
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2016 NeuralGenerativeQuestionAnsweri | Hang Li Zhengdong Lu Xiaoming Li Jun Yin Xin Jiang Lifeng Shang | Neural Generative Question Answering | 2016 |