Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings

Proceedings of the 28th International Conference on Computational Linguistics, pages 5879–5890 Barcelona, Spain (Online), December 8-13, 2020 5879 Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings Arun Ramachandran Microsoft Hyderabad, India ramachandran.arun@outlook.com Gerard de Melo Hasso Plattner Institute, University of Potsdam Potsdam, Germany gdm@demelo.org Abstract Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually for over 350 lan- guages, many of them resource-poor, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction. 1 Introduction Two main forms of classifying emotions are often distinguished: representing them along continuous dimensions, or breaking them into discrete categories (Stevenson et al., 2007; Calvo and Kim, 2013). A prominent instance of the former approach is the PAD model by Russell and Mehrabian (1977), which represents affect along 3 dimensions: pleasure, arousal, and dominance. An example of the latter is the Wheel of Emotions by Plutchik (1980), who argued that most emotions can be derived from a set of eight basic ones – anger, fear, sadness, disgust, surprise, anticipation, trust, and joy. There have been efforts to create emotion lexicons, where each word is assigned either scores or discrete classes reflecting the associated emotions. Such lexicons are useful in emotional analyses of product reviews, literary texts, or posts on social media, inter alia. Bradley et al. (1999) solicited human affective norm ratings to create such a dataset for English based on the PAD model. Mohammad and Turney (2013) relied on crowdsourcing to annotate words with Plutchik’s 8 basic emotions, providing binary labels. The recent NRC Emotion Intensity Lexicon (Mohammad, 2018) reconciles the notion of discrete emotions, corresponding to commonly invoked emotion names, with the benefits of continuous scoring in accounting for degrees of emotion intensity. Again relying on crowdsourcing, the lexicon provides intensity scores for Plutchik’s eight basic emotions. Affective norm ratings have as well been procured for certain other languages. An alternative route is to draw on automated techniques such as machine translation, as has been done for the NRC Emotion Intensity lexicon, where the English words are translated to other languages using Google Translate while retaining the original scores. Buechel et al. (2020) used Google Translate to translate a source emotion lexicon to a target lexicon that serves as training data, based on which valence/arousal/dominance or 5 basic emotions are predicted for a range of resource-rich languages. However, at the time of writing this paper, Google Translate serves around 100 languages. This raises the question of whether similar resources can be induced for resource-poor languages using minuscule amounts of data. In this paper, we investigate simple means of deriving …

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