Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem

By Sven Buechel, Udo Hahn. 2018

35 citations recorded by the source corpus.

Predicting the emotional value of lexical items is a well-known problem in sentiment analysis. While research has focused on polarity for quite a long time, meanwhile this early focus has been shifted to more expressive emotion representation models (such as Basic Emotions or Valence-Arousal-Dominance). This change resulted in a proliferation of heterogeneous formats and, in parallel, often small-sized, non-interoperable resources (lexicons and corpus annotations). In particular, the limitations in size hampered the application of deep learning methods in this area because they typically require large amounts of input data. We here present a solution to get around this language data bottleneck by rephrasing word emotion induction as a multi-task learning problem. In this approach, the prediction of each independent emotion dimension is considered as an individual task and hidden layers are shared between these dimensions. We investigate whether multi-task learning is more advantageous than single-task learning for emotion prediction by comparing our model against a wide range of alternative emotion and polarity induction methods featuring 9 typologically diverse languages and a total of 15 conditions. Our model turns out to outperform each one of them. Against all odds, the proposed deep learning approach yields the largest gain on the smallest data sets, merely composed of one thousand samples.

Subfields: Artificial Intelligence, Natural Language Processing, Sentiment & Opinion Analysis

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Citations in the corpus

Modeling Empathy and Distress in Reaction to News Stories (2018, methodology): “Only very recently, researchers started exploring more sophisticated models of human emotion on a larger scale (Wang et al., 2016; Abdul-Mageed and Ungar, 2017; Mohammad and Bravo-Marquez, 2017a; Buechel and Hahn, 2017, 2018a,b).”
Learning and Evaluating Emotion Lexicons for 91 Languages (2020, methodology): “…mapping (ERM), an annotation projection that translates ratings from one emotion format into another, e.g., mapping VAD labels into BE5, or vice versa (Hoffmann et al., 2012; Buechel and Hahn, 2016, 2018a; Alarcão and Fonseca, 2017; Landowska, 2018; Zhou et al., 2020; Park et al., 2019).”