QAScore-An Unsupervised Unreferenced Metric for the Question Generation Evaluation.

Area of Science:

• Natural Language Processing
• Artificial Intelligence

Background:

• Automated Question Generation (QG) has advanced with neural models, but evaluation remains a challenge.
• Current metrics like BLEU and BERTScore rely on references and show low agreement with human judgment.
• Existing metrics for QG systems do not consider the passage or answer context.

Purpose of the Study:

• To introduce QAScore, a novel reference-free evaluation metric for Question Generation.
• To provide a more accurate and human-aligned method for assessing QG system performance.
• To address the limitations of current QG evaluation metrics.

Main Methods:

• QAScore evaluates generated questions by measuring a language model's ability to predict masked answer words.
• The metric computes cross-entropy based on the probability of correctly generating masked words within the answer.
• A human evaluation experiment was conducted to compare QAScore with existing metrics.

Main Results:

• QAScore demonstrates a stronger correlation with human judgment compared to BLEU and BERTScore.
• The proposed metric offers improved evaluation accuracy for Question Generation systems.
• Human evaluation confirmed the superiority of QAScore in assessing question quality.

Conclusions:

• QAScore provides a more reliable and accurate evaluation for Question Generation systems.
• Reference-free evaluation using QAScore enhances the assessment of automated question quality.
• This metric facilitates better development and benchmarking of QG technologies.