TransferNet：一种有效透明的关系图多跳问答框架
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph
摘要：
多跳问答（QA）是一个具有挑战性的任务，因为它需要在每一步的答案与实体关系的精确推理。关系可以根据知识图谱中的标签来表示（例如，\textit{spouse}）或文本语料库中的文本（例如，\textit{they have been married for 26 years}）。现有的模型通常通过预测序列关系路径或聚集隐藏图特征来推断答案。前者很难优化，后者缺乏可解释性。在本文中，我们提出了TransferNet，一个有效的和透明的多跳QA模型，它支持在一个统一的框架中的标签和文本关系。TransferNet以多个步骤跨越实体。在每一步中，它关注问题的不同部分，计算关系的激活分数，然后以可微的方式沿着激活的关系传递先前的实体分数。我们在三个数据集上进行了广泛的实验，并证明了TransferNet在很大程度上超越了最先进的模型。特别是在MetaQA上，它在2跳和3跳问题中达到了100%的准确率。通过定性分析，我们表明，TransferNet具有透明和可解释的中间结果。
英文摘要：
Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represented in terms of labels in knowledge graph (e.g., \textit{spouse}) or text in text corpus (e.g., \textit{they have been married for 26 years}). Existing models usually infer the answer by predicting the sequential relation path or aggregating the hidden graph features. The former is hard to optimize, and the latter lacks interpretability. In this paper, we propose TransferNet, an effective and transparent model for multi-hop QA, which supports both label and text relations in a unified framework. TransferNet jumps across entities at multiple steps. At each step, it attends to different parts of the question, computes activated scores for relations, and then transfer the previous entity scores along activated relations in a differentiable way. We carry out extensive experiments on three datasets and demonstrate that TransferNet surpasses the state-of-the-art models by a large margin. In particular, on MetaQA, it achieves 100\% accuracy in 2-hop and 3-hop questions. By qualitative analysis, we show that TransferNet has transparent and interpretable intermediate results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
