Multi-Source cross-lingual transfer learning deals with the transfer of task
knowledge from multiple labelled source languages to an unlabeled target
language under the language shift. Existing methods typically focus on
weighting the predictions produced by language-specific classifiers of
different sources that follow a shared encoder. However, all source languages
share the same encoder, which is updated by all these languages. The extracted
representations inevitably contain different source languages' information,
which may disturb the learning of the language-specific classifiers.
Additionally, due to the language gap, language-specific classifiers trained
with source labels are unable to make accurate predictions for the target
language. Both facts impair the model's performance. To address these
challenges, we propose a Disentangled and Adaptive Network (DA-Net). Firstly,
we devise a feedback-guided collaborative disentanglement method that seeks to
purify input representations of classifiers, thereby mitigating mutual
interference from multiple sources. Secondly, we propose a class-aware parallel
adaptation method that aligns class-level distributions for each source-target
language pair, thereby alleviating the language pairs' language gap.
Experimental results on three different tasks involving 38 languages validate
the effectiveness of our approach.

多源跨语言迁移学习中，通过语言转移从多个标记的源语言传递任务知识到一个未标记的目标语言。本文提出了一种分离和自适应网络（DA-Net），通过反馈引导合作解缠方法清洁分类器的输入表示，从而减轻多个源的干扰；并提出了一种类别感知的并行适应方法，对每个源 - 目标语言对齐类别级别的分布，从而减轻语言对之间的语言差距。实验结果验证了我们方法的有效性，涉及 38 种语言的三个不同任务。

DA-Net：多源跨语言迁移学习的解缠和自适应网络

DA-Net: A Disentangled and Adaptive Network for Multi-Source  Cross-Lingual Transfer Learning

This paper is about the development of a machine translation system tailored
specifically for LATEX mathematical documents. The system focuses on
translating English LATEX mathematical documents into Arabic LATEX, catering to
the growing demand for multilingual accessibility in scientific and
mathematical literature. With the vast proliferation of LATEX mathematical
documents the need for an efficient and accurate translation system has become
increasingly essential. This paper addresses the necessity for a robust
translation tool that enables seamless communication and comprehension of
complex mathematical content across language barriers. The proposed system
leverages a Transformer model as the core of the translation system, ensuring
enhanced accuracy and fluency in the translated Arabic LATEX documents.
Furthermore, the integration of RyDArab, an Arabic mathematical TEX extension,
along with a rule-based translator for Arabic mathematical expressions,
contributes to the precise rendering of complex mathematical symbols and
equations in the translated output. The paper discusses the architecture,
methodology, of the developed system, highlighting its efficacy in bridging the
language gap in the domain of mathematical documentation

该论文讨论了开发一种专门针对 LATEX 数学文档的机器翻译系统，将英文 LATEX 数学文档翻译成阿拉伯 LATEX，并关注满足科学和数学文献多语言可访问性的不断增长的需求。通过 Transformer 模型作为翻译系统的核心，与 RyDArab 的集成和基于规则的阿拉伯数学表达式翻译器相结合，确保在翻译成阿拉伯 LATEX 文档中准确呈现复杂数学符号和方程式，从而弥合数学文档领域的语言障碍。

数学文档的英语至阿拉伯语机器翻译

English to Arabic machine translation of mathematical documents

We present research towards bridging the language gap between migrant workers
in Qatar and medical staff. In particular, we present the first steps towards
the development of a real-world Hindi-English machine translation system for
doctor-patient communication. As this is a low-resource language pair,
especially for speech and for the medical domain, our initial focus has been on
gathering suitable training data from various sources. We applied a variety of
methods ranging from fully automatic extraction from the Web to manual
annotation of test data. Moreover, we developed a method for automatically
augmenting the training data with synthetically generated variants, which
yielded a very sizable improvement of more than 3 BLEU points absolute.

本研究聚焦于解决卡塔尔移民工人与医务人员之间的语言障碍问题，尤其是通过开发一种真实可行的印地语 - 英语机器翻译系统来提高医生和患者之间的交流质量，我们采用了各种方法来收集适合的训练数据，并成功地通过生成合成变量的方法自动扩充训练数据，从而实现了 BLEU 分数绝对值提高超过 3 分。