论文标题
朝着标签敏捷的情感嵌入
Towards Label-Agnostic Emotion Embeddings
论文作者
论文摘要
情绪分析中的研究分散在不同的标签格式（例如极性类型，基本情感类别和情感维度），语言水平（单词与句子与话语），当然（当然，很少有良好的但更不明显的文本）自然语言和文本类型（例如，产品评论，tweets，新闻，新闻）。由此产生的异质性使得在这些相互矛盾的约束下开发的数据和软件难以比较和挑战以集成。为了解决这种不令人满意的事务状态，我们在这里提出了一种培训计划，该计划可以学习共享的情绪的潜在表示，独立于不同的标签格式，自然语言，甚至不同的模型体系结构。在各种数据集上进行的实验表明，这种方法可产生所需的互操作性，而无需惩罚预测质量。代码和数据在DOI 10.5281/Zenodo.5466068下存档。
Research in emotion analysis is scattered across different label formats (e.g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few well-resourced but much more under-resourced) natural languages and text genres (e.g., product reviews, tweets, news). The resulting heterogeneity makes data and software developed under these conflicting constraints hard to compare and challenging to integrate. To resolve this unsatisfactory state of affairs we here propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. Experiments on a wide range of datasets indicate that this approach yields the desired interoperability without penalizing prediction quality. Code and data are archived under DOI 10.5281/zenodo.5466068.
