Data augmentation has the potential to improve the performance of machine
learning models by increasing the amount of training data available. In this
study, we evaluated the effectiveness of different data augmentation techniques
for a multi-label emotion classification task using a low-resource dataset. Our
results showed that Back Translation outperformed autoencoder-based approaches
and that generating multiple examples per training instance led to further
performance improvement. In addition, we found that Back Translation generated
the most diverse set of unigrams and trigrams. These findings demonstrate the
utility of Back Translation in enhancing the performance of emotion
classification models in resource-limited situations.

本研究评估了不同数据增强技术对低资源数据集上多标签情绪分类任务的效果，并发现后向翻译技术超过基于自编码器的方法，每个训练实例生成多个示例进一步提高了性能，此外后向翻译生成了最多样化的单字和三字组合，这些发现证明了后向翻译在资源有限情况下提升情绪分类模型性能的实用性。

评估数据增强在低资源环境下情感分类的有效性

Evaluating the Effectiveness of Data Augmentation for Emotion  Classification in Low-Resource Settings

Emotion recognition (ER) is an important task in Natural Language Processing
(NLP), due to its high impact in real-world applications from health and
well-being to author profiling, consumer analysis and security. Current
approaches to ER, mainly classify emotions independently without considering
that emotions can co-exist. Such approaches overlook potential ambiguities, in
which multiple emotions overlap. We propose a new model "SpanEmo" casting
multi-label emotion classification as span-prediction, which can aid ER models
to learn associations between labels and words in a sentence. Furthermore, we
introduce a loss function focused on modelling multiple co-existing emotions in
the input sentence. Experiments performed on the SemEval2018 multi-label
emotion data over three language sets (i.e., English, Arabic and Spanish)
demonstrate our method's effectiveness. Finally, we present different analyses
that illustrate the benefits of our method in terms of improving the model
performance and learning meaningful associations between emotion classes and
words in the sentence.

提出了一种名为 SpanEmo 的新模型，将多标签情感分类视为跨度预测，并引入专注于对输入句子中多重共存情感进行建模的损失函数，实验证明了我们方法在提高模型性能和学习情感类别与句中单词之间有意义的关联方面的优势。