论文标题
双线性与基于注意的NLI模型的常识知识的融合融合
Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models
论文作者
论文摘要
我们考虑将现实世界常识知识纳入深度自然语言推理（NLI）模型的任务。现有的外部知识合并方法仅限于词汇水平知识，并且在NLI模型，数据集和常识性知识来源之间缺乏概括。为了解决这些问题，我们提出了一种新型NLI模型独立的神经框架BICAM。 BICAM将现实世界的常识知识纳入NLI模型中。 BICAM与卷积特征探测器和双线性特征融合结合在一起，提供了一种概念上简单的机制，可以很好地推广。与ConceptNet和Aristo Tuple kgs结合使用的SNLI和Scitail数据集上的两个最先进的NLI基准的定量评估表明，BICAM大大提高了融合的NLI碱基的准确性。例如，我们的BIECAM模型是BICAM的实例，在具有挑战性的Scitail数据集中，与ConceptNet的融合基线的准确性提高了7.0％，而Aristo Tuple kg则提高了8.0％。
We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical level knowledge and lack generalization across NLI models, datasets, and commonsense knowledge sources. To address these issues, we propose a novel NLI model-independent neural framework, BiCAM. BiCAM incorporates real-world commonsense knowledge into NLI models. Combined with convolutional feature detectors and bilinear feature fusion, BiCAM provides a conceptually simple mechanism that generalizes well. Quantitative evaluations with two state-of-the-art NLI baselines on SNLI and SciTail datasets in conjunction with ConceptNet and Aristo Tuple KGs show that BiCAM considerably improves the accuracy the incorporated NLI baselines. For example, our BiECAM model, an instance of BiCAM, on the challenging SciTail dataset, improves the accuracy of incorporated baselines by 7.0% with ConceptNet, and 8.0% with Aristo Tuple KG.
