Improving Robustness of Machine Translation with Synthetic Noise

2019/02/25 by Vaibhav Vaibhav, Vaibhav, Vaibhav, Sumeet Singh +5 · 2 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling #cs.CL #cs.LG

paper · pdf · doi:10.48550/arxiv.1902.09508

Accepted at NAACL 2019

openalex publication_date 2019/02/25 · arxiv created 2019/04/10 · arxiv updated 2019/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Modern Machine Translation (MT) systems perform consistently well on clean, in-domain text. However most human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of output translation. In this paper we leverage the Machine Translation of Noisy Text (MTNT) dataset to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system resilient to naturally occurring noise and partially mitigate loss in accuracy resulting therefrom.

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• In-Domain African Languages Translation Using LLMs and Multi-armed Bandits
• Different Perturbations, Different Mechanisms: Understanding Continued Pre-training for Zero-Shot Dialect Robustness

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