CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (1811.00937v2)

Abstract: When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering. To capture common sense beyond associations, we extract from ConceptNet (Speer et al., 2017) multiple target concepts that have the same semantic relation to a single source concept. Crowd-workers are asked to author multiple-choice questions that mention the source concept and discriminate in turn between each of the target concepts. This encourages workers to create questions with complex semantics that often require prior knowledge. We create 12,247 questions through this procedure and demonstrate the difficulty of our task with a large number of strong baselines. Our best baseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy, well below human performance, which is 89%.

• The paper presents a novel benchmark designed to evaluate AI systems' commonsense reasoning using a challenging, crowd-sourced multiple-choice dataset.
• It employs both feature-based and advanced neural network models to rigorously assess performance on nuanced commonsense questions.
• Findings highlight a significant performance gap between AI models and humans, underscoring the need for improved commonsense knowledge integration.

CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

The paper "CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge" presents a novel benchmark designed to evaluate the commonsense reasoning abilities of AI systems. Authored by Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant, the paper focuses on a structured methodology for assessing and enhancing the performance of models in understanding and applying commonsense knowledge.

Dataset Creation and Analysis

The researchers introduce the CommonsenseQA dataset, a set of multiple-choice questions grounded in commonsense knowledge. This dataset stands out due to the diversity and complexity of the questions, which were expressly designed to go beyond mere fact retrieval and instead require an understanding of nuanced and implicit information. The dataset generation involved crowdworkers who were carefully instructed to create questions that would be challenging for AI systems but straightforward for humans possessing commonsense knowledge.

A comprehensive analysis of the dataset is performed to ensure quality and rigor. The statistical distribution of answer choices, the variety of concepts covered, and the difficulty levels are meticulously verified. The validity of the dataset is further reinforced through human performance evaluation, establishing a benchmark for comparing future AI systems.

Baseline Models and Experimental Setup

The authors evaluate several baseline models on the CommonsenseQA dataset, including classical approaches and state-of-the-art neural architectures. They employ various methodologies such as lexical matching, feature-based models, and advanced neural models:

• Feature-based models: Utilize manually engineered features informed by linguistic insights.
• Neural network approaches: Includes architectures like BERT and GPT, known for their efficacy in capturing contextually rich representations.

Results indicate a significant performance gap between AI systems and human baseline, highlighting the challenging nature of the dataset. The best-performing neural models achieved accuracy rates considerably lower than human performance, showcasing the complexity of commonsense reasoning tasks.

Implications and Future Directions

The introduction of CommonsenseQA has profound implications for the development and evaluation of AI systems. It provides a robust framework for assessing a critical aspect of intelligence—commonsense reasoning—that is often overlooked in traditional benchmarks. The stark contrast between human and model performance underscores the need for further advancements in AI's understanding of commonsense knowledge.

Theoretically, the benchmark presents an opportunity to explore the intricacies of model interpretability and reasoning capabilities. It encourages researchers to develop novel architectures that can effectively integrate external knowledge sources and perform reasoning tasks that mirror human cognitive processes.

Practically, improving performance on CommonsenseQA has potential applications across numerous domains, including natural language understanding, dialogue systems, and autonomous agents. As AI systems become more proficient in commonsense reasoning, their utility and reliability in real-world scenarios will be greatly enhanced.

Conclusion

"CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge" provides a significant contribution to the field of AI and NLP by offering a challenging and comprehensive dataset focused on commonsense reasoning. The findings underscore the current limitations of AI systems and set the stage for future research dedicated to bridging the gap between human and machine understanding of commonsense knowledge. As research progresses, benchmarks like CommonsenseQA will be instrumental in guiding the development of more sophisticated and contextually aware AI systems.