mlsanityData to 14 Oct 2026

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

arXiv 2101.11707 ↗First posted 27 Jan 2021cs.CL cs.AI cs.LOcited by 12

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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.

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.

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12 of 32 references.

References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2015Towards AI-Complete Question Answering: A Set of Prerequisite Toy Taskscited by 314
  4. 2015Ask Me Anything: Dynamic Memory Networks for Natural Language Processingcited by 117
  5. 2016Learning End-to-End Goal-Oriented Dialogcited by 104
  6. 2018Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systemscited by 70
  7. 2018Constraint Answer Set Programming without Groundingcited by 38
  8. 2018Disentangling Language and Knowledge in Task-Oriented Dialogscited by 17
  9. 2020Justifications for Goal-Directed Constraint Answer Set Programmingcited by 14
  10. 2020SQuARE: Semantics-based Question Answering and Reasoning Enginecited by 4
  11. 2018Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computercited by 3
  12. 2019Conversational AI : Open Domain Question Answering and Commonsense Reasoningcited by 3

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Cited by year

Citing papers per year: 2021: 5; 2022: 3; 2023: 2; 2024: 2.

12 papers.

  1. 2023Reliable Natural Language Understanding with Large Language Models and Answer Set Programmingcited by 12
  2. 2023Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogscited by 12
  3. 2021CASPR: A Commonsense Reasoning-based Conversational Socialbotcited by 6
  4. 2021An ASP-based Approach to Answering Natural Language Questions for Textscited by 3
  5. 2022A Survey on Knowledge-Enhanced Pre-trained Language Modelscited by 3
  6. 2024EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learningcited by 3
  7. 2021DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbotscited by 2
  8. 2024TIC: Translate-Infer-Compile for accurate "text to plan" using LLMs and Logical Representationscited by 2
  9. 2021Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programmingcited by 1
  10. 2021Towards Dynamic Consistency Checking in Goal-directed Predicate Answer Set Programmingcited by 1
  11. 2022Automating UAV Flight Readiness Approval using Goal-Directed Answer Set Programmingcited by 0
  12. 2022A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoodscited by 0