# Six Challenges for Neural Machine Translation

## TL;DR

This paper identifies six key challenges in neural machine translation, evaluates their impact on translation quality, and compares neural methods with traditional phrase-based systems.

## Contribution

It systematically explores six major challenges in neural machine translation and assesses their effects relative to phrase-based methods.

## Key findings

- Neural MT faces significant issues with domain mismatch and rare words.
- Long sentences and beam search also pose notable challenges.
- Neural methods show both improvements and deficiencies compared to phrase-based MT.

## Abstract

We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.