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.

本文提出了一种训练方案，可以学习独立于不同标签格式、自然语言甚至不同模型架构的情感的共享潜在表示，通过实验表明，该方法能够实现所需的互操作性，而不降低预测准确率。

面向标签无关情感嵌入

Towards Label-Agnostic Emotion Embeddings

We present a deep generative model of bilingual sentence pairs for machine
translation. The model generates source and target sentences jointly from a
shared latent representation and is parameterised by neural networks. We
perform efficient training using amortised variational inference and
reparameterised gradients. Additionally, we discuss the statistical
implications of joint modelling and propose an efficient approximation to
maximum a posteriori decoding for fast test-time predictions. We demonstrate
the effectiveness of our model in three machine translation scenarios:
in-domain training, mixed-domain training, and learning from a mix of
gold-standard and synthetic data. Our experiments show consistently that our
joint formulation outperforms conditional modelling (i.e. standard neural
machine translation) in all such scenarios.

我们提出了一种基于神经网络的双语句子对深度生成模型来进行机器翻译，能够通过共享的潜在表示联合生成源语言句子和目标语言句子，并使用摊余变分推断和重参数化梯度进行高效的训练，可以在领域内、混合领域数据、金标准和合成数据混合学习等各种情况下显示出比条件建模（即标准神经机器翻译）更好的性能。同时，我们讨论了联合建模的统计学意义，并提出了一种高效的近似方法来进行最大后验解码，以便在测试时进行快速预测。