# Cross-lingual Emotion Detection

arXiv: https://arxiv.org/abs/2106.06017 · HTML: https://mlsanity.com/paper/2106.06017/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus](https://mlsanity.com/paper/2004.06295/) (2020)
- [Arabic Dialect Identification in the Wild](https://mlsanity.com/paper/2005.06557/) (2020)
- [Arabic Offensive Language on Twitter: Analysis and Experiments](https://mlsanity.com/paper/2004.02192/) (2020)
- [Zero-Shot Cross-Lingual Opinion Target Extraction](https://mlsanity.com/paper/1904.09122/) (2019)
- [Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank](https://mlsanity.com/paper/1909.02235/) (2019)
- [Cross-lingual Distillation for Text Classification](https://mlsanity.com/paper/1705.02073/) (2017)

## Built on by (most cited first, 5 of 5)

- [Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech](https://mlsanity.com/paper/2201.06723/) (2022)
- [D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias](https://mlsanity.com/paper/2305.17013/) (2023)
- [Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training](https://mlsanity.com/paper/2111.06181/) (2021)
- [ReDDIT: Regret Detection and Domain Identification from Text](https://mlsanity.com/paper/2212.07549/) (2022)
- [Moonworks Lunara Aesthetic Dataset](https://mlsanity.com/paper/2601.07941/) (2026)

## Abstract (arXiv)

Emotion detection can provide us with a window into understanding human behavior. Due to the complex dynamics of human emotions, however, constructing annotated datasets to train automated models can be expensive. Thus, we explore the efficacy of cross-lingual approaches that would use data from a source language to build models for emotion detection in a target language. We compare three approaches, namely: i) using inherently multilingual models; ii) translating training data into the target language; and iii) using an automatically tagged parallel corpus. In our study, we consider English as the source language with Arabic and Spanish as target languages. We study the effectiveness of different classification models such as BERT and SVMs trained with different features. Our BERT-based monolingual models that are trained on target language data surpass state-of-the-art (SOTA) by 4% and 5% absolute Jaccard score for Arabic and Spanish respectively. Next, we show that using cross-lingual approaches with English data alone, we can achieve more than 90% and 80% relative effectiveness of the Arabic and Spanish BERT models respectively. Lastly, we use LIME to analyze the challenges of training cross-lingual models for different language pairs