# Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs

arXiv: https://arxiv.org/abs/2103.07766 · HTML: https://mlsanity.com/paper/2103.07766/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 13 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [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)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018)
- [Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision](https://mlsanity.com/paper/1611.00020/) (2016)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [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)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base](https://mlsanity.com/paper/1910.05069/) (2019)
- [Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning](https://mlsanity.com/paper/2001.10468/) (2020)

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

- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [Conversational Question Answering: A Survey](https://mlsanity.com/paper/2106.00874/) (2021)
- [Conversational Question Answering on Heterogeneous Sources](https://mlsanity.com/paper/2204.11677/) (2022)
- [UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text](https://mlsanity.com/paper/2108.08614/) (2021)
- [Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2109.00269/) (2021)
- [GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering](https://mlsanity.com/paper/2303.13284/) (2023)
- [History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling](https://mlsanity.com/paper/2306.06872/) (2023)
- [VOGUE: Answer Verbalization through Multi-Task Learning](https://mlsanity.com/paper/2106.13316/) (2021)
- [Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2210.04373/) (2022)
- [An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs](https://mlsanity.com/paper/2208.06734/) (2022)

## Abstract (arXiv)

Neural semantic parsing approaches have been widely used for Question Answering (QA) systems over knowledge graphs. Such methods provide the flexibility to handle QA datasets with complex queries and a large number of entities. In this work, we propose a novel framework named CARTON, which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph. Our framework consists of a stack of pointer networks as an extension of a context transformer model for parsing the input question and the dialog history. The framework generates a sequence of actions that can be executed on the knowledge graph. We evaluate CARTON on a standard dataset for complex sequential question answering on which CARTON outperforms all baselines. Specifically, we observe performance improvements in F1-score on eight out of ten question types compared to the previous state of the art. For logical reasoning questions, an improvement of 11 absolute points is reached.