# Machine translation considering context information using Encoder-Decoder model

## TL;DR

This paper introduces a novel encoder-decoder model for machine translation that effectively incorporates context information from preceding sentences, leading to improved translation quality.

## Contribution

It proposes a new model that integrates context information into the encoder-decoder framework, enhancing translation accuracy over existing models.

## Key findings

- Higher translation score than existing models
- Effective integration of preceding sentence context
- Improved translation quality

## Abstract

In the task of machine translation, context information is one of the important factor. But considering the context information model dose not proposed. The paper propose a new model which can integrate context information and make translation. In this paper, we create a new model based Encoder Decoder model. When translating current sentence, the model integrates output from preceding encoder with current encoder. The model can consider context information and the result score is higher than existing model.