论文标题
Leapmood：轻有效的体系结构，以遗传算法驱动的超参数调整来预测情绪
LEAPMood: Light and Efficient Architecture to Predict Mood with Genetic Algorithm driven Hyperparameter Tuning
论文作者
论文摘要
精确和自动检测情绪可作为用户概况（例如用户分析）的构建块，这些案例又有电源应用，例如广告，推荐系统等。指示个人情绪的一个主要来源是文本数据。尽管人们对情感识别进行了广泛的研究，但情绪预测领域几乎没有得到探索。此外，在开发项目推断领域的工作很少，从用户隐私的角度来看，这非常重要。在本文中，我们首次提出了一种从文本数据，Leapmood中进行情绪预测的内在深度学习方法。我们根据遗传算法（GA）使用一种新颖的以设备部署为重点的目标函数进行超参数调整，并优化有关性能和大小的参数。 LeapMood由转换（ERC）中的情感识别组成，作为第一个构建块，然后使用K-Means群集进行情绪预测。我们表明，使用角色嵌入，语音散列和注意力以及条件随机场（CRF）的组合，导致性能与当前最新面前的性能相当，而ERC任务的模型大小（> 90％）显着降低。我们在DailyDialog数据集上仅获得1.67MB的记忆足迹，我们的Micro F1得分为62.05％。此外，我们为情绪预测的任务策划了一个数据集，以Leapmood的身份达到72.12％的宏F1得分。
Accurate and automatic detection of mood serves as a building block for use cases like user profiling which in turn power applications such as advertising, recommendation systems, and many more. One primary source indicative of an individual's mood is textual data. While there has been extensive research on emotion recognition, the field of mood prediction has been barely explored. In addition, very little work is done in the area of on-device inferencing, which is highly important from the user privacy point of view. In this paper, we propose for the first time, an on-device deep learning approach for mood prediction from textual data, LEAPMood. We use a novel on-device deployment-focused objective function for hyperparameter tuning based on the Genetic Algorithm (GA) and optimize the parameters concerning both performance and size. LEAPMood consists of Emotion Recognition in Conversion (ERC) as the first building block followed by mood prediction using K-means clustering. We show that using a combination of character embedding, phonetic hashing, and attention along with Conditional Random Fields (CRF), results in a performance closely comparable to that of the current State-Of-the-Art with a significant reduction in model size (> 90%) for the task of ERC. We achieve a Micro F1 score of 62.05% with a memory footprint of a mere 1.67MB on the DailyDialog dataset. Furthermore, we curate a dataset for the task of mood prediction achieving a Macro F1-score of 72.12% with LEAPMood.
