We investigate two research questions: (1) how do machine translation (MT)
and diacritization influence the performance of each other in a multi-task
learning setting (2) the effect of keeping (vs. removing) diacritics on MT
performance. We examine these two questions in both high-resource (HR) and
low-resource (LR) settings across 55 different languages (36 African languages
and 19 European languages). For (1), results show that diacritization
significantly benefits MT in the LR scenario, doubling or even tripling
performance for some languages, but harms MT in the HR scenario. We find that
MT harms diacritization in LR but benefits significantly in HR for some
languages. For (2), MT performance is similar regardless of diacritics being
kept or removed. In addition, we propose two classes of metrics to measure the
complexity of a diacritical system, finding these metrics to correlate
positively with the performance of our diacritization models. Overall, our work
provides insights for developing MT and diacritization systems under different
data size conditions and may have implications that generalize beyond the 55
languages we investigate.

我们研究了机器翻译和变音符号在多任务学习环境中相互影响的表现以及在机器翻译性能上保留（与去除）变音符号的影响。在 55 种不同语言（36 种非洲语言和 19 种欧洲语言）的高资源和低资源设置中，我们发现：对于第一个问题，变音符号对低资源情况下的机器翻译性能有重要的帮助作用，对某些语言甚至使性能提升两倍或三倍，但对高资源情况下的机器翻译性能有害；我们发现对于某些语言，机器翻译对低资源情况下的变音符号处理有害，但对高资源情况下的处理有显著好处。对于第二个问题，无论保留还是去除变音符号，机器翻译性能都相似。此外，我们提出了两类度量变音符号系统复杂性的指标，发现这些指标与我们的变音符号模型的性能呈正相关。总的来说，我们的研究为在不同的数据规模条件下开发机器翻译和变音符号系统提供了洞见，并可能具有超越我们研究范围的 55 种语言的普适意义。