Multi-turn Inference Matching Network for Natural Language Inference
This work addresses a fundamental NLP task with an incremental improvement in model design for better accuracy.
The paper tackles the problem of Natural Language Inference by proposing a Multi-turn Inference Matching Network (MIMN) that performs multi-turn inference on different matching features with a memory component, achieving state-of-the-art performance on three NLI datasets.
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.