An all-too-present bottleneck for text classification model development is
the need to annotate training data and this need is multiplied for multilingual
classifiers. Fortunately, contemporary machine translation models are both
easily accessible and have dependable translation quality, making it possible
to translate labeled training data from one language into another. Here, we
explore the effects of using machine translation to fine-tune a multilingual
model for a classification task across multiple languages. We also investigate
the benefits of using a novel technique, originally proposed in the field of
image captioning, to account for potential negative effects of tuning models on
translated data. We show that translated data are of sufficient quality to tune
multilingual classifiers and that this novel loss technique is able to offer
some improvement over models tuned without it.

利用机器翻译来调整多语言模型以进行分类任务在多种语言之间产生一定的效果，并且通过一种新颖的技术可以改善模型对翻译数据的负面影响。

利用机器翻译增强多语言分类

Using Machine Translation to Augment Multilingual Classification

Health departments have been deploying text classification systems for the
early detection of foodborne illness complaints in social media documents such
as Yelp restaurant reviews. Current systems have been successfully applied for
documents in English and, as a result, a promising direction is to increase
coverage and recall by considering documents in additional languages, such as
Spanish or Chinese. Training previous systems for more languages, however,
would be expensive, as it would require the manual annotation of many documents
for each new target language. To address this challenge, we consider
cross-lingual learning and train multilingual classifiers using only the
annotations for English-language reviews. Recent zero-shot approaches based on
pre-trained multi-lingual BERT (mBERT) have been shown to effectively align
languages for aspects such as sentiment. Interestingly, we show that those
approaches are less effective for capturing the nuances of foodborne illness,
our public health application of interest. To improve performance without extra
annotations, we create artificial training documents in the target language
through machine translation and train mBERT jointly for the source (English)
and target language. Furthermore, we show that translating labeled documents to
multiple languages leads to additional performance improvements for some target
languages. We demonstrate the benefits of our approach through extensive
experiments with Yelp restaurant reviews in seven languages. Our classifiers
identify foodborne illness complaints in multilingual reviews from the Yelp
Challenge dataset, which highlights the potential of our general approach for
deployment in health departments.

本研究提出了一种跨语言学习方法，通过机器翻译构建人工训练文档，训练出多语言分类器，从而实现在多语言文档中进行食源性疾病的早期探测，具有很好的应用前景。