This paper describes the University of Maryland's submission to the Special
Task on Formality Control for Spoken Language Translation at \iwslt, which
evaluates translation from English into 6 languages with diverse grammatical
formality markers. We investigate to what extent this problem can be addressed
with a \textit{single multilingual model}, simultaneously controlling its
output for target language and formality. Results show that this strategy can
approach the translation quality and formality control achieved by dedicated
translation models. However, the nature of the underlying pre-trained language
model and of the finetuning samples greatly impact results.

研究了单一的多语种模型在控制目标语言和形式上的输出，在解决从英语翻译成六种具有不同语法正式标记的语言方面的问题上，可以接近专用翻译模型实现的翻译质量和形式控制，但是预训练语言模型和微调样本的性质对结果有很大的影响。

利用预训练的多语言语言模型控制翻译形式

Controlling Translation Formality Using Pre-trained Multilingual Language Models

This paper describes the SLT-CDT-UoS group's submission to the first Special
Task on Formality Control for Spoken Language Translation, part of the IWSLT
2022 Evaluation Campaign. Our efforts were split between two fronts: data
engineering and altering the objective function for best hypothesis selection.
We used language-independent methods to extract formal and informal sentence
pairs from the provided corpora; using English as a pivot language, we
propagated formality annotations to languages treated as zero-shot in the task;
we also further improved formality controlling with a hypothesis re-ranking
approach. On the test sets for English-to-German and English-to-Spanish, we
achieved an average accuracy of .935 within the constrained setting and .995
within unconstrained setting. In a zero-shot setting for English-to-Russian and
English-to-Italian, we scored average accuracy of .590 for constrained setting
and .659 for unconstrained.

本文介绍了 SLT-CDT-UoS 团队参加 IWSLT 2022 评估活动中形式控制特别任务的贡献。我们在数据工程和目标函数改变上付出了努力，使用语言独立方法从提供的语料库中提取正式和非正式句子对，使用英语作为中间语言，我们将格式注释传播到任务中被视为零炮击的语言，并通过假设重新排名方法进一步改善格式控制。在英语到德语和英语到西班牙语的测试集中，我们在约束环境下的平均准确率为 0.935，在无约束环境下的平均准确率为 0.995，在英语到俄语和英语到意大利的零炮击环境中，我们得分在约束环境下的平均准确率为 0.590，在无约束环境下的平均准确率为 0.659。