Abstract
State-of-the-art KBQA models assume answerability of questions. Recent research has shown that while these can be adapted to detect unaswerability with suitable training and thresholding, this comes at the expense of accuracy for answerable questions, and no single model is able to handle all categories of unanswerability. We propose a new model for KBQA named RetinaQA that is robust against unaswerability. It complements KB-traversal based logical form retrieval with sketch-filling based logical form construction. This helps with questions that have valid logical forms but no data paths in the KB leading to an answer. Additionally, it uses discrimination instead of generation to better identify questions that do not have valid logical forms. We demonstrate that RetinaQA significantly outperforms adaptations of state-of-the-art KBQA models across answerable and unanswerable questions, while showing robustness across unanswerability categories. Remarkably, it also establishes a new state-of-the art for answerable KBQA by surpassing existing models
Abstract (translated)
先进的KBQA模型假定问题有答案。最近的研究表明，虽然这些模型可以适应性地检测不具答案的问题，但代价是对答案问题的准确性，而且没有一个模型能够处理所有类别的无答案问题。我们提出了一种名为RetinaQA的新模型，对无答案问题具有鲁棒性。它与基于逻辑形式检索的KB-遍历和基于填充的逻辑形式构建相结合。这有助于那些具有有效逻辑形式但无KB路径回答问题的提问。此外，它使用区分而不是生成来更好地识别没有有效逻辑形式的提问。我们证明了RetinaQA在答案问题和无答案问题上的先进模型改编方面显著优于其他模型，而保持对无答案问题的鲁棒性。值得注意的是，它还在答案问题的KBQA领域树立了新的标杆，超越了现有模型。
URL
https://arxiv.org/abs/2403.10849
