# Encoding Source Language with Convolutional Neural Network for Machine Translation

arXiv: https://arxiv.org/abs/1503.01838 · HTML: https://mlsanity.com/paper/1503.01838/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2015.

## Built on by (most cited first, 4 of 4)

- [Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts](https://mlsanity.com/paper/2309.05312/) (2023)
- [THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses](https://mlsanity.com/paper/2105.13630/) (2021)
- [Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts](https://mlsanity.com/paper/2309.12863/) (2023)
- [Unified Model Learning for Various Neural Machine Translation](https://mlsanity.com/paper/2305.02777/) (2023)

## Abstract (arXiv)

The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information. With different guiding signals during decoding, our specifically designed convolution+gating architectures can pinpoint the parts of a source sentence that are relevant to predicting a target word, and fuse them with the context of entire source sentence to form a unified representation. This representation, together with target language words, are fed to a deep neural network (DNN) to form a stronger NNJM. Experiments on two NIST Chinese-English translation tasks show that the proposed model can achieve significant improvements over the previous NNJM by up to +1.08 BLEU points on average