Generative Knowledge Question Answering Technology Based on Global Coverage Mechanism and Representation Learning
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摘要: 针对现有生成式问答模型中陌生词汇导致答案准确率低下的问题和模式混乱导致的词汇重复问题, 本文提出引入知识表示学习结果的方法提高模型识别陌生词汇的能力, 提高模型准确率. 同时本文提出使用全局覆盖机制以平衡不同模式答案生成的概率, 减少由预测模式混乱导致的重复输出问题, 提高答案的质量. 本文在知识问答模型基础上结合知识表示学习的推理结果, 使模型具备模糊回答的能力. 在合成数据集和现实世界数据集上的实验证明了本模型能够有效地提高生成答案的质量, 能对推理知识进行模糊回答.Abstract: Aiming at the problem of low answer accuracy caused by unfamiliar words in the existing generative question answering model and the problem of vocabulary repetition caused by pattern confusion, this paper proposes a method of introducing knowledge representation learning results to improve the model＇s ability to recognize unfamiliar words and improve the accuracy of the model. At the same time, this paper proposes to use a global coverage mechanism to balance the probability of answer generation in different modes, reduce the repeated output problem caused by the confusion of prediction modes, and improve the quality of the answer. Based on the knowledge question answering model, this paper combines the inference results of knowledge representation learning, so that the model has the ability to answer fuzzy answers. Experiments on synthetic datasets and real-world datasets demonstrate that this model can effectively improve the quality of generated answers and can provide fuzzy answers to reasoning knowledge.
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图 4 MCQA (TE, CE)与CoreQA 答案对比样例
Fig. 4 The comparison of MCQA (TE, CE) and CoreQA sample outputs
表 1 问答数据集规模
Table 1 The size of QA datasets
数据集 问答对数量 关系数量 SimpleQuestions 101 754 1 631 生日问答数据集 239 922 5 社区问答数据集 505 021 4 011 
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表 3 生日数据集实验结果 (%)
Table 3 The experimental results of birthday datasets (%)
方法 $ {{P}_{g}} $ $ {{P}_{y}} $ $ {{P}_{m}} $ $ {{P}_{d}} $ $ {{P}_{r}} $ Seq2Seq 67.3 — 23.4 — 37.2 NMT 71.6 — 27.1 — 54.7 CopyNet 75.2 — — — 71.9 GenQA (本文) 73.4 63.2 65.8 77.1 62.6 CoreQA 75.6 84.8 93.4 81 80.3 MCQA (WE, CE) 89.8 89.1 98.4 93.2 84.1 MCQA (TE, CE) 88.6 89.4 98.7 93.6 84.6
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表 4 社区问答实验结果 (%)
Table 4 The experimental results of community QA datasets (%)
方法 正确性 流畅性 一致性 CopyNet — 19.4 21.3 GenQA (本文) 24.3 38.3 24.1 CoreQA 49.3 51.8 62.5 MCQA (WE, CE) 52.3 55.8 65.2 MCQA (TE, CE) 54.1 56.3 65.0
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表 5 模糊问答推理结果 (%)
Table 5 The prediction results of ambiguously QA (%)
方法 $ {{P}_{y}} $ $ {{P}_{m}} $ $ {{P}_{d}} $ PTransE 93.2 97.4 95.0
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表 6 模糊问答结果 (%)
Table 6 The results of ambiguously QA (%)
方法 $ {{F1}_{t}} $ $ {{P}_{y}} $ $ {{P}_{m}} $ $ {{P}_{d}} $ $ {{P}_{r}} $ MCQA (WE, CE) 87.7 78.1 88.2 90.8 80.9
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