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Joint emotion label space modeling for affect lexica

机译：影响Lexica的联合情感标签空间建模

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摘要

Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection. However, vocabulary coverage issues, differences in construction method and discrepancies in emotion framework and representation result in a heterogeneous landscape of emotion detection resources, calling for a unified approach to utilizing them. To combat this, we present an extended emotion lexicon of 30,273 unique entries, which is a result of merging eight existing emotion lexica by means of a multi-view variational autoencoder (VAE). We showed that a VAE is a valid approach for combining lexica with different label spaces into a joint emotion label space with a chosen number of dimensions, and that these dimensions are still interpretable. We tested the utility of the unified VAE lexicon by employing the lexicon values as features in an emotion detection model. We found that the VAE lexicon outperformed individual lexica, but contrary to our expectations, it did not outperform a naive concatenation of lexica, although it did contribute to the naive concatenation when added as an extra lexicon. Furthermore, using lexicon information as additional features on top of state-of-the-art language models usually resulted in a better performance than when no lexicon information was used.
机译：情感Lexica是常用的资源来解决自动情绪检测中的数据贫困。然而，词汇覆盖问题，施工方法的差异和情感框架的差异和表示的差异导致情感检测资源的异质景观，呼吁利用它们的统一方法。为了打击这一点，我们展示了30,273个唯一条目的扩展情绪词典，这是通过多视图变形式自动化器（VAE）合并八个现有情感Lexica的结果。我们认为VAE是将Lexica与不同标签空间组合成具有所选尺寸数量的联合情绪标签空间的有效方法，并且这些尺寸仍然是可解释的。我们通过在情感检测模型中使用Lexicon值作为特征来测试Unified VAE词汇的实用程序。我们发现VAE词典优于个别莱克里卡，但与我们的期望相反，它并没有超越Lexica的天真连接，尽管当它作为额外的词典添加时，它确实有助于天真的连接。此外，使用Lexicon信息作为最先进的语言模型上的附加功能，通常导致比使用Lexicon信息的更好的性能。

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