# Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types

arXiv: https://arxiv.org/abs/1802.06842 · HTML: https://mlsanity.com/paper/1802.06842/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 17 of 17)

- [Asking It All: Generating Contextualized Questions for any Semantic Role](https://mlsanity.com/paper/2109.04832/) (2021)
- [Transformer-based Entity Typing in Knowledge Graphs](https://mlsanity.com/paper/2210.11151/) (2022)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction](https://mlsanity.com/paper/2110.08345/) (2021)
- [AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL](https://mlsanity.com/paper/2208.12461/) (2022)
- [How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering](https://mlsanity.com/paper/2401.05777/) (2024)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [Synthetic Context Generation for Question Generation](https://mlsanity.com/paper/2406.13188/) (2024)
- [A Single Example Can Improve Zero-Shot Data Generation](https://mlsanity.com/paper/2108.06991/) (2021)
- [Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization](https://mlsanity.com/paper/2302.04415/) (2023)
- [Adapting Knowledge for Few-shot Table-to-Text Generation](https://mlsanity.com/paper/2302.12468/) (2023)
- [Selective Token Generation for Few-shot Natural Language Generation](https://mlsanity.com/paper/2209.08206/) (2022)
- [KGConv, a Conversational Corpus grounded in Wikidata](https://mlsanity.com/paper/2308.15298/) (2023)
- [Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing](https://mlsanity.com/paper/2401.11972/) (2024)
- [Calculating Question Similarity is Enough: A New Method for KBQA Tasks](https://mlsanity.com/paper/2111.07658/) (2021)
- [How to Build Robust FAQ Chatbot with Controllable Question Generator?](https://mlsanity.com/paper/2112.03007/) (2021)
- [Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2604.10748/) (2026)

## Abstract (arXiv)

We present a neural model for question generation from knowledge base triples in a "Zero-Shot" setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in an encoder-decoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model sets a new state-of-the-art for zero-shot QG.