# Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection

## TL;DR

This paper introduces a black-box method to inject gender and number information into neural machine translation outputs, improving translation accuracy without altering the original model.

## Contribution

It presents a novel black-box approach for controlling morphological features in NMT, enhancing translation quality by injecting missing gender and number information.

## Key findings

- Effective in injecting gender and number information
- Improves BLEU score by up to 2.3 on female-speaker test set
- Validated through syntactic analysis of translations

## Abstract

When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must "guess" this missing information, often leading to incorrect translations in the given context. We propose a black-box approach for injecting the missing information to a pre-trained neural machine translation system, allowing to control the morphological variations in the generated translations without changing the underlying model or training data. We evaluate our method on an English to Hebrew translation task, and show that it is effective in injecting the gender and number information and that supplying the correct information improves the translation accuracy in up to 2.3 BLEU on a female-speaker test set for a state-of-the-art online black-box system. Finally, we perform a fine-grained syntactic analysis of the generated translations that shows the effectiveness of our method.