Speech translation (ST) systems translate speech in one language to text in
another language. End-to-end ST systems (e2e-ST) have gained popularity over
cascade systems because of their enhanced performance due to reduced latency
and computational cost. Though resource intensive, e2e-ST systems have the
inherent ability to retain para and non-linguistic characteristics of the
speech unlike cascade systems. In this paper, we propose to use an e2e
architecture for English-Hindi (en-hi) ST. We use two imperfect machine
translation (MT) services to translate Libri-trans en text into hi text. While
each service gives MT data individually to generate parallel ST data, we
propose a data augmentation strategy of noisy MT data to aid robust ST. The
main contribution of this paper is the proposal of a data augmentation
strategy. We show that this results in better ST (BLEU score) compared to brute
force augmentation of MT data. We observed an absolute improvement of 1.59 BLEU
score with our approach.

该论文介绍了使用端到端系统和噪声机器翻译数据增强策略来改进语音翻译，提高 BLEU 分数，该方法应用于英语 - 印地语语音翻译。

选择性数据增强对于强健的语音翻译的影响

Selective Data Augmentation for Robust Speech Translation

We present findings from a first in-depth post-editing effort estimation
study in the English-Hindi direction along multiple effort indicators. We
conduct a controlled experiment involving professional translators, who
complete assigned tasks alternately, in a translation from scratch and a
post-edit condition. We find that post-editing reduces translation time (by
63%), utilizes fewer keystrokes (by 59%), and decreases the number of pauses
(by 63%) when compared to translating from scratch. We further verify the
quality of translations thus produced via a human evaluation task in which we
do not detect any discernible quality differences.

本文通过对英印文翻译的后编辑效率调查，证明后编辑技术可将翻译时间缩短 63%，击键次数减少 59% 且屏息次数减少 63%，同时还没有明显影响翻译质量。

评估英印方向的编辑后工作量

Assessing Post-editing Effort in the English-Hindi Direction

In this paper we explore various parameter settings of the state-of-art
Statistical Machine Translation system to improve the quality of the
translation for a `distant' language pair like English-Hindi. We proposed new
techniques for efficient reordering. A slight improvement over the baseline is
reported using these techniques. We also show that a simple pre-processing step
can improve the quality of the translation significantly.

本文旨在探究各种参数设置下最先进的统计机器翻译系统，以提高像英语 - 印地语这样的 “远距离” 语言对翻译的质量，并提出了新的有效的重排序技术和简单预处理步骤，以显著改善翻译结果的质量。