Controlling Translation Formality Using Pre-trained Multilingual Language Models

被引：0

作者：

Rippeth, Elijah ^{[1
]}

Agrawal, Sweta ^{[1
]}

Carpuat, Marine ^{[1
]}

机构：

[1] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA

来源：

PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE TRANSLATION (IWSLT 2022) | 2022年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 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.

引用

页码：327 / 340

页数：14

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