# Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model

arXiv: https://arxiv.org/abs/1806.04466 · HTML: https://mlsanity.com/paper/1806.04466/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Document-Level Language Models for Machine Translation](https://mlsanity.com/paper/2310.12303/) (2023)
- [Challenges in Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2305.13751/) (2023)
- [Exploring Paracrawl for Document-level Neural Machine Translation](https://mlsanity.com/paper/2304.10216/) (2023)
- [Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context](https://mlsanity.com/paper/2311.15507/) (2023)
- [Addressing the Length Bias Problem in Document-Level Neural Machine Translation](https://mlsanity.com/paper/2311.11601/) (2023)

## Abstract (arXiv)

Neural machine translation (NMT) systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring inter-sentence information. This may make the translation of a sentence ambiguous or even inconsistent with the translations of neighboring sentences. In order to handle this issue, we propose an inter-sentence gate model that uses the same encoder to encode two adjacent sentences and controls the amount of information flowing from the preceding sentence to the translation of the current sentence with an inter-sentence gate. In this way, our proposed model can capture the connection between sentences and fuse recency from neighboring sentences into neural machine translation. On several NIST Chinese-English translation tasks, our experiments demonstrate that the proposed inter-sentence gate model achieves substantial improvements over the baseline.