# Multi-turn Inference Matching Network for Natural Language Inference

## TL;DR

The paper introduces MIMN, a multi-turn inference model for NLI that processes different matching features separately and uses memory to improve interaction, achieving state-of-the-art results.

## Contribution

It proposes a novel multi-turn inference framework with memory for NLI, focusing on separate matching features to enhance inference accuracy.

## Key findings

- Outperforms existing models on three NLI datasets.
- Achieves state-of-the-art performance across all tested datasets.
- Demonstrates the effectiveness of multi-turn inference with memory in NLI.

## Abstract

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.