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

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 languages, 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.

Guilt by Association: Emotion Intensities in Lexical Representations

“…The task of emotional association of words has been studied on other languages as well. Sidorov et al (2012) presents a dataset of Spanish words labelled with Ekman's six emotions, while others explored cross-lingual propagation from one language to another (Abdaoui et al, 2017) or to hundreds of other languages (Ramachandran and de Melo, 2020). We show that we can make use of linguistic models to obtain high correlations with emotion intensities without any need for manual ratings.…”

Inferring Multilingual Domain-Specific Word Embeddings From Large Document Corpora

“…To this purpose, a bilingual lexicon is used to map the words of a source language (e.g., English) to the corresponding translations. Aligned word embedding models have been exploited to effectively address cross-lingual NLP tasks, such as cross-lingual text classification [20], emotion lexicon induction [21], cross-lingual summarization [22]. As a drawback, in many cross-lingual NLP scenarios the use of aligned multilingual word embeddings is still limited by the lack of pre-trained domain-specific models for languages other than English.…”