# Neural Generative Question Answering

arXiv: https://arxiv.org/abs/1512.01337 · HTML: https://mlsanity.com/paper/1512.01337/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2015.

## Built on by (most cited first, 22 of 22)

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- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering](https://mlsanity.com/paper/2306.04136/) (2023)
- [Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering](https://mlsanity.com/paper/2309.11206/) (2023)
- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [Parameter-Efficient Abstractive Question Answering over Tables or Text](https://mlsanity.com/paper/2204.03357/) (2022)
- [Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems](https://mlsanity.com/paper/2203.10610/) (2022)
- [A Neural Question Answering System for Basic Questions about Subroutines](https://mlsanity.com/paper/2101.03999/) (2021)
- [VANiLLa : Verbalized Answers in Natural Language at Large Scale](https://mlsanity.com/paper/2105.11407/) (2021)
- [Learning to Transpile AMR into SPARQL](https://mlsanity.com/paper/2112.07877/) (2021)
- [An Explainable Framework for Misinformation Identification via Critical Question Answering](https://mlsanity.com/paper/2503.14626/) (2025)
- [Information Discovery in e-Commerce](https://mlsanity.com/paper/2410.05763/) (2024)
- [Generating Diverse Descriptions from Semantic Graphs](https://mlsanity.com/paper/2108.05659/) (2021)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [Natural Response Generation for Chinese Reading Comprehension](https://mlsanity.com/paper/2302.08817/) (2023)
- [Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering](https://mlsanity.com/paper/2408.15037/) (2024)
- [Recent Progress in Conversational AI](https://mlsanity.com/paper/2204.09719/) (2022)
- [Knowledge Graph Quality Evaluation under Incomplete Information](https://mlsanity.com/paper/2212.00994/) (2022)
- [Contextual Dictionary Lookup for Knowledge Graph Completion](https://mlsanity.com/paper/2306.07719/) (2023)
- [DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion](https://mlsanity.com/paper/2306.10290/) (2023)

## Abstract (arXiv)

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.