# Knowledge-driven Natural Language Understanding of English Text and its Applications

arXiv: https://arxiv.org/abs/2101.11707 · HTML: https://mlsanity.com/paper/2101.11707/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://mlsanity.com/paper/1502.05698/) (2015)
- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://mlsanity.com/paper/1506.07285/) (2015)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/1804.08217/) (2018)
- [Constraint Answer Set Programming without Grounding](https://mlsanity.com/paper/1804.11162/) (2018)
- [Disentangling Language and Knowledge in Task-Oriented Dialogs](https://mlsanity.com/paper/1805.01216/) (2018)
- [Justifications for Goal-Directed Constraint Answer Set Programming](https://mlsanity.com/paper/2009.10238/) (2020)
- [SQuARE: Semantics-based Question Answering and Reasoning Engine](https://mlsanity.com/paper/2009.10239/) (2020)
- [Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer](https://mlsanity.com/paper/1809.02444/) (2018)
- [Conversational AI : Open Domain Question Answering and Commonsense Reasoning](https://mlsanity.com/paper/1909.08258/) (2019)

## Built on by (most cited first, 12 of 12)

- [Reliable Natural Language Understanding with Large Language Models and Answer Set Programming](https://mlsanity.com/paper/2302.03780/) (2023)
- [Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs](https://mlsanity.com/paper/2303.08941/) (2023)
- [CASPR: A Commonsense Reasoning-based Conversational Socialbot](https://mlsanity.com/paper/2110.05387/) (2021)
- [An ASP-based Approach to Answering Natural Language Questions for Texts](https://mlsanity.com/paper/2112.11241/) (2021)
- [A Survey on Knowledge-Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2212.13428/) (2022)
- [EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning](https://mlsanity.com/paper/2403.10692/) (2024)
- [DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbots](https://mlsanity.com/paper/2109.08297/) (2021)
- [TIC: Translate-Infer-Compile for accurate "text to plan" using LLMs and Logical Representations](https://mlsanity.com/paper/2402.06608/) (2024)
- [Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programming](https://mlsanity.com/paper/2109.04634/) (2021)
- [Towards Dynamic Consistency Checking in Goal-directed Predicate Answer Set Programming](https://mlsanity.com/paper/2110.12053/) (2021)
- [Automating UAV Flight Readiness Approval using Goal-Directed Answer Set Programming](https://mlsanity.com/paper/2208.12199/) (2022)
- [A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods](https://mlsanity.com/paper/2211.16822/) (2022)

## Abstract (arXiv)

Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. By leveraging the VerbNet lexicon, we are able to map syntax tree of the text to its commonsense meaning represented using basic knowledge primitives. The general purpose knowledge represented from our approach can be used to build any reasoning based NLU system that can also provide justification. We applied this approach to construct two NLU applications that we present here: SQuARE (Semantic-based Question Answering and Reasoning Engine) and StaCACK (Stateful Conversational Agent using Commonsense Knowledge). Both these systems work by "truly understanding" the natural language text they process and both provide natural language explanations for their responses while maintaining high accuracy.