Natural Language Inference (NLI) is a fundamental and challenging task in Natural Language Processing (NLP). Most existing methods only apply one-pass inference process on a mixed matching feature, which is a concatenation of different matching features between a premise and a hypothesis. In this paper, we propose a new model called Multi-turn Inference Matching Network (MIMN) to perform multi-turn inference on different matching features. In each turn, the model focuses on one particular matching feature instead of the mixed matching feature. To enhance the interaction between different matching features, a memory component is employed to store the history inference information. The inference of each turn is performed on the current matching feature and the memory. We conduct experiments on three different NLI datasets. The experimental results show that our model outperforms or achieves the state-of-the-art performance on all the three datasets.
翻译:自然语言推断(NLI)是自然语言处理(NLP)中一项根本性和具有挑战性的任务。大多数现有方法只对混合匹配特征应用一次性推断过程,即将前提和假设的不同匹配特征混为一体。在本文中,我们提出一个新的模型,称为多方向推断匹配网络(MIMN),以对不同的匹配特征进行多重推论。在每一回合中,模型侧重于一个特定的匹配特征,而不是混合匹配特征。为了加强不同匹配特征之间的相互作用,使用一个记忆部分来存储历史推断信息。每个转折的推论是在当前匹配特征和记忆上进行的。我们用三个不同的NLI数据集进行实验。实验结果显示,我们的模型在全部三个数据集上都优于或达到最新性能。