[Paper Review] Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches

Shane Storks, Qiaozi Gao+1 more|arXiv (Cornell University)|Apr 2, 2019

Topic Modeling46 citations

TL;DR

This survey provides a comprehensive overview of benchmarks, knowledge resources, and learning/inference approaches for commonsense reasoning in NLP. It synthesizes existing work to clarify the state of the art, identify limitations, and highlight future challenges in enabling machines to understand natural language through human-like reasoning.

ABSTRACT

Commonsense knowledge and commonsense reasoning are some of the main bottlenecks in machine intelligence. In the NLP community, many benchmark datasets and tasks have been created to address commonsense reasoning for language understanding. These tasks are designed to assess machines' ability to acquire and learn commonsense knowledge in order to reason and understand natural language text. As these tasks become instrumental and a driving force for commonsense research, this paper aims to provide an overview of existing tasks and benchmarks, knowledge resources, and learning and inference approaches toward commonsense reasoning for natural language understanding. Through this, our goal is to support a better understanding of the state of the art, its limitations, and future challenges.

Motivation & Objective

• To synthesize existing benchmarks and datasets designed to evaluate commonsense reasoning in natural language understanding.
• To analyze available knowledge resources that support commonsense reasoning in NLP systems.
• To examine learning and inference methods used to improve machine reasoning with commonsense knowledge.
• To identify gaps and limitations in current approaches and guide future research in the field.

Proposed method

• Systematic review and categorization of existing benchmark datasets focused on commonsense reasoning in NLP.
• Analysis of knowledge resources such as ConceptNet, Open information extraction tools, and knowledge graphs used to ground commonsense reasoning.
• Survey of learning approaches including pretraining, fine-tuning, and knowledge-augmented neural networks for commonsense reasoning.
• Examination of inference techniques that integrate symbolic and neural methods to improve reasoning over natural language.
• Evaluation of task design patterns across benchmarks to identify common challenges and evaluation criteria.
• Synthesis of trends, limitations, and open problems in current research directions.

Experimental results

Research questions

• RQ1 What are the key benchmarks and datasets currently used to evaluate commonsense reasoning in NLP?
• RQ2 Which knowledge resources are most effective in supporting commonsense reasoning tasks?
• RQ3 How do current learning and inference methods compare in terms of performance and generalization?
• RQ4 What are the main limitations and open challenges in advancing commonsense reasoning in NLP?
• RQ5 How can future research build on existing work to achieve more robust and human-like reasoning in machines?

Key findings

• A wide range of benchmarks have been developed to evaluate commonsense reasoning, including ARC, CommonsenseQA, and HellaSwag, each targeting different reasoning capabilities.
• Knowledge resources such as ConceptNet and Open information extraction systems are widely used to supply external commonsense knowledge to NLP models.
• Pretraining-based models fine-tuned on commonsense tasks show significant performance gains, but generalization remains limited across domains.
• Hybrid approaches combining symbolic knowledge with neural networks show promise in improving reasoning robustness and interpretability.
• Despite progress, current systems still struggle with out-of-distribution reasoning and complex causal or counterfactual reasoning.
• The field lacks standardized evaluation protocols and shared benchmarks, hindering fair comparison across methods.