# On the Capabilities of Pointer Networks for Deep Deductive Reasoning

arXiv: https://arxiv.org/abs/2106.09225 · HTML: https://mlsanity.com/paper/2106.09225/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Logic Tensor Networks](https://mlsanity.com/paper/2012.13635/) (2020)
- [Neural-Symbolic Learning and Reasoning: A Survey and Interpretation](https://mlsanity.com/paper/1711.03902/) (2017)
- [End-to-End Differentiable Proving](https://mlsanity.com/paper/1705.11040/) (2017)
- [Learning Reasoning Strategies in End-to-End Differentiable Proving](https://mlsanity.com/paper/2007.06477/) (2020)
- [Code Completion with Neural Attention and Pointer Networks](https://mlsanity.com/paper/1711.09573/) (2017)
- [Ontology Reasoning with Deep Neural Networks](https://mlsanity.com/paper/1808.07980/) (2018)
- [Stack-Pointer Networks for Dependency Parsing](https://mlsanity.com/paper/1805.01087/) (2018)
- [Differentiable Reasoning on Large Knowledge Bases and Natural Language](https://mlsanity.com/paper/1912.10824/) (2019)
- [Can Neural Networks Understand Logical Entailment?](https://mlsanity.com/paper/1802.08535/) (2018)
- [Left-to-Right Dependency Parsing with Pointer Networks](https://mlsanity.com/paper/1903.08445/) (2019)
- [Transition-based Semantic Dependency Parsing with Pointer Networks](https://mlsanity.com/paper/2005.13344/) (2020)
- [Online Conversation Disentanglement with Pointer Networks](https://mlsanity.com/paper/2010.11080/) (2020)
- [Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction](https://mlsanity.com/paper/1803.05976/) (2018)
- [Completion Reasoning Emulation for the Description Logic EL+](https://mlsanity.com/paper/1912.05063/) (2019)
- [PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs](https://mlsanity.com/paper/2009.00106/) (2020)

## Built on by (most cited first, 4 of 4)

- [Generalizable Neuro-symbolic Systems for Commonsense Question Answering](https://mlsanity.com/paper/2201.06230/) (2022)
- [Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems](https://mlsanity.com/paper/2311.07759/) (2023)
- [Differentiable Fuzzy ALC: A Neural-Symbolic Representation Language for Symbol Grounding](https://mlsanity.com/paper/2211.12006/) (2022)
- [Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges](https://mlsanity.com/paper/2308.04814/) (2023)

## Abstract (arXiv)

The importance of building neural networks that can learn to reason has been well recognized in the neuro-symbolic community. In this paper, we apply neural pointer networks for conducting reasoning over symbolic knowledge bases. In doing so, we explore the benefits and limitations of encoder-decoder architectures in general and pointer networks in particular for developing accurate, generalizable and robust neuro-symbolic reasoners. Based on our experimental results, pointer networks performs remarkably well across multiple reasoning tasks while outperforming the previously reported state of the art by a significant margin. We observe that the Pointer Networks preserve their performance even when challenged with knowledge graphs of the domain/vocabulary it has never encountered before. To the best of our knowledge, this is the first study on neuro-symbolic reasoning using Pointer Networks. We hope our impressive results on these reasoning problems will encourage broader exploration of pointer networks' capabilities for reasoning over more complex logics and for other neuro-symbolic problems.