# English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts

arXiv: https://arxiv.org/abs/2402.03223 · HTML: https://mlsanity.com/paper/2402.03223/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 58 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 30 of 31)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://mlsanity.com/paper/2012.15828/) (2020)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://mlsanity.com/paper/2301.10472/) (2023)
- [Do Multilingual Language Models Think Better in English?](https://mlsanity.com/paper/2308.01223/) (2023)
- [Few-Shot Text Generation with Pattern-Exploiting Training](https://mlsanity.com/paper/2012.11926/) (2020)
- [Multilingual Language Models are not Multicultural: A Case Study in Emotion](https://mlsanity.com/paper/2307.01370/) (2023)
- [Discrete and Soft Prompting for Multilingual Models](https://mlsanity.com/paper/2109.03630/) (2021)
- [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://mlsanity.com/paper/2011.01612/) (2020)
- [Emotion Intensities in Tweets](https://mlsanity.com/paper/1708.03696/) (2017)
- [Crowdsourcing and Validating Event-focused Emotion Corpora for German and English](https://mlsanity.com/paper/1905.13618/) (2019)
- [Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt](https://mlsanity.com/paper/2202.11451/) (2022)
- [Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models](https://mlsanity.com/paper/2210.12360/) (2022)
- [Polyglot Prompt: Multilingual Multitask PrompTraining](https://mlsanity.com/paper/2204.14264/) (2022)
- [Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training](https://mlsanity.com/paper/2111.06181/) (2021)

## Built on by (most cited first, 7 of 7)

- [Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas](https://mlsanity.com/paper/2408.06929/) (2024)
- [Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models](https://mlsanity.com/paper/2403.10258/) (2024)
- [Which Demographics do LLMs Default to During Annotation?](https://mlsanity.com/paper/2410.08820/) (2024)
- [Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching](https://mlsanity.com/paper/2410.18436/) (2024)
- [From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyrics](https://mlsanity.com/paper/2509.05617/) (2025)
- [Decoding Neighborhood Environments with Large Language Models](https://mlsanity.com/paper/2505.08163/) (2025)
- [Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition](https://mlsanity.com/paper/2505.18040/) (2025)

## Abstract (arXiv)

Emotion classification in text is a challenging task due to the processes involved when interpreting a textual description of a potential emotion stimulus. In addition, the set of emotion categories is highly domain-specific. For instance, literature analysis might require the use of aesthetic emotions (e.g., finding something beautiful), and social media analysis could benefit from fine-grained sets (e.g., separating anger from annoyance) than only those that represent basic categories as they have been proposed by Paul Ekman (anger, disgust, fear, joy, surprise, sadness). This renders the task an interesting field for zero-shot classifications, in which the label set is not known at model development time. Unfortunately, most resources for emotion analysis are English, and therefore, most studies on emotion analysis have been performed in English, including those that involve prompting language models for text labels. This leaves us with a research gap that we address in this paper: In which language should we prompt for emotion labels on non-English texts? This is particularly of interest when we have access to a multilingual large language model, because we could request labels with English prompts even for non-English data. Our experiments with natural language inference-based language models show that it is consistently better to use English prompts even if the data is in a different language.