# Semantic Parsing for Conversational Question Answering over Knowledge Graphs

arXiv: https://arxiv.org/abs/2301.12217 · HTML: https://mlsanity.com/paper/2301.12217/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 28 Jan 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Measuring Compositional Generalization: A Comprehensive Method on Realistic Data](https://mlsanity.com/paper/1912.09713/) (2019)
- [COGS: A Compositional Generalization Challenge Based on Semantic Interpretation](https://mlsanity.com/paper/2010.05465/) (2020)
- [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://mlsanity.com/paper/1909.05378/) (2019)
- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [SParC: Cross-Domain Semantic Parsing in Context](https://mlsanity.com/paper/1906.02285/) (2019)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Improving Text-to-SQL Evaluation Methodology](https://mlsanity.com/paper/1806.09029/) (2018)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures](https://mlsanity.com/paper/2007.08970/) (2020)
- [A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions](https://mlsanity.com/paper/2105.11644/) (2021)
- [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018)
- [Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion](https://mlsanity.com/paper/1910.03262/) (2019)
- [Conversational Information Seeking](https://mlsanity.com/paper/2201.08808/) (2022)
- [On Compositional Generalization of Neural Machine Translation](https://mlsanity.com/paper/2105.14802/) (2021)
- [Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base](https://mlsanity.com/paper/1910.05069/) (2019)
- [Conversational Question Answering on Heterogeneous Sources](https://mlsanity.com/paper/2204.11677/) (2022)
- [Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks](https://mlsanity.com/paper/2104.01569/) (2021)
- [Learning to Map Context-Dependent Sentences to Executable Formal Queries](https://mlsanity.com/paper/1804.06868/) (2018)
- [Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2109.00269/) (2021)
- [Memory-Based Semantic Parsing](https://mlsanity.com/paper/2110.07358/) (2021)

## Built on by (most cited first, 10 of 10)

- [Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts](https://mlsanity.com/paper/2405.06524/) (2024)
- [Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation](https://mlsanity.com/paper/2310.13505/) (2023)
- [Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2401.01711/) (2024)
- [Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering](https://mlsanity.com/paper/2409.04181/) (2024)
- [Towards Adaptive Context Management for Intelligent Conversational Question Answering](https://mlsanity.com/paper/2509.17829/) (2025)
- [Conversational Semantic Parsing using Dynamic Context Graphs](https://mlsanity.com/paper/2305.06164/) (2023)
- [KGConv, a Conversational Corpus grounded in Wikidata](https://mlsanity.com/paper/2308.15298/) (2023)
- [A Survey of the State-of-the-Art in Conversational Question Answering Systems](https://mlsanity.com/paper/2509.05716/) (2025)
- [SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2512.04868/) (2025)
- [Structure-Token Evidence-Anchored Reasoning for Scientific Chart Understanding](https://mlsanity.com/paper/2609.13267/) (2026)

## Abstract (arXiv)

In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.