Building a QnA Dataset from Large Texts and Summaries: Dealing with False Negatives in Answer Matching – Need Validation Workarounds!
Analysis
This post highlights a common challenge in creating QnA datasets: validating the accuracy of automatically generated question-answer pairs, especially when dealing with large datasets. The author's approach of using cosine similarity on embeddings to find matching answers in summaries often leads to false negatives. The core problem lies in the limitations of relying solely on semantic similarity metrics, which may not capture the nuances of language or the specific context required for a correct answer. The need for automated or semi-automated validation methods is crucial to ensure the quality of the dataset and, consequently, the performance of the QnA system. The post effectively frames the problem and seeks community input for potential solutions.
Key Takeaways
- •Validating QnA datasets is crucial for system performance.
- •Cosine similarity alone is insufficient for accurate answer matching.
- •Automated or semi-automated validation methods are needed for large datasets.
“This approach gives me a lot of false negative sentences. Since the dataset is huge, manual checking isn't feasible.”