# Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors

## TL;DR

This paper investigates how neural machine translation systems handle errors from automatic speech recognition, comparing their robustness to traditional phrase-based models in translating spoken language outputs.

## Contribution

It introduces the problem of translating ASR outputs with NMT systems and compares their robustness against phrase-based models, highlighting the strengths of NMT in handling speech recognition errors.

## Key findings

- NMT systems show different robustness levels to ASR errors compared to phrase-based models.
- Word-based encoding impacts the translation quality of ASR outputs.
- Certain ASR error phenomena are better captured by NMT models.

## Abstract

Machine translation systems are conventionally trained on textual resources that do not model phenomena that occur in spoken language. While the evaluation of neural machine translation systems on textual inputs is actively researched in the literature , little has been discovered about the complexities of translating spoken language data with neural models. We introduce and motivate interesting problems one faces when considering the translation of automatic speech recognition (ASR) outputs on neural machine translation (NMT) systems. We test the robustness of sentence encoding approaches for NMT encoder-decoder modeling, focusing on word-based over byte-pair encoding. We compare the translation of utterances containing ASR errors in state-of-the-art NMT encoder-decoder systems against a strong phrase-based machine translation baseline in order to better understand which phenomena present in ASR outputs are better represented under the NMT framework than approaches that represent translation as a linear model.