Machine translation considering context information using Encoder-Decoder model
This addresses the need for better context-aware translation in NLP, but it is incremental as it builds on standard encoder-decoder architectures.
The paper tackled the problem of incorporating context information in machine translation by proposing a new encoder-decoder model that integrates outputs from preceding encoders with current ones, resulting in higher scores than existing models.
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.