# Neural Machine Translation with Noisy Lexical Constraints

## TL;DR

This paper introduces a novel neural machine translation framework that effectively manages noisy lexical constraints by treating them as external memories, leading to improved translation quality in practical scenarios.

## Contribution

The proposed approach uniquely handles noisy constraints by treating them as external memories, enabling correction and improving translation performance.

## Key findings

- Achieves substantial BLEU gains with noisy constraints.
- Improves translation quality with automatically generated constraints.
- Demonstrates robustness to constraint errors.

## Abstract

Lexically constrained decoding for machine translation has shown to be beneficial in previous studies. Unfortunately, constraints provided by users may contain mistakes in real-world situations. It is still an open question that how to manipulate these noisy constraints in such practical scenarios. We present a novel framework that treats constraints as external memories. In this soft manner, a mistaken constraint can be corrected. Experiments demonstrate that our approach can achieve substantial BLEU gains in handling noisy constraints. These results motivate us to apply the proposed approach on a new scenario where constraints are generated without the help of users. Experiments show that our approach can indeed improve the translation quality with the automatically generated constraints.