# Neural Machine Translation for Bilingually Scarce Scenarios: A Deep Multi-task Learning Approach

arXiv: https://arxiv.org/abs/1805.04237 · HTML: https://mlsanity.com/paper/1805.04237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 2 of 2)

- [A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning](https://mlsanity.com/paper/2407.03076/) (2024)
- [An Evaluation of Persian-English Machine Translation Datasets with Transformers](https://mlsanity.com/paper/2302.00321/) (2023)

## Abstract (arXiv)

Neural machine translation requires large amounts of parallel training text to learn a reasonable-quality translation model. This is particularly inconvenient for language pairs for which enough parallel text is not available. In this paper, we use monolingual linguistic resources in the source side to address this challenging problem based on a multi-task learning approach. More specifically, we scaffold the machine translation task on auxiliary tasks including semantic parsing, syntactic parsing, and named-entity recognition. This effectively injects semantic and/or syntactic knowledge into the translation model, which would otherwise require a large amount of training bitext. We empirically evaluate and show the effectiveness of our multi-task learning approach on three translation tasks: English-to-French, English-to-Farsi, and English-to-Vietnamese.