# Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora arXiv: https://arxiv.org/abs/2209.06701 ยท HTML: https://mlsanity.com/paper/2209.06701/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 56 references that are in the map. - First posted on arXiv 14 Sep 2022. ## Builds on (most cited first, 21 of 21) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017) - [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020) - [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017) - [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019) - [Zero-Shot Learning Through Cross-Modal Transfer](https://mlsanity.com/paper/1301.3666/) (2013) - [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013) - [EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis](https://mlsanity.com/paper/2205.01996/) (2022) - [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022) - [GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception](https://mlsanity.com/paper/1912.03184/) (2019) - [Emotion Intensities in Tweets](https://mlsanity.com/paper/1708.03696/) (2017) - [Appraisal Theories for Emotion Classification in Text](https://mlsanity.com/paper/2003.14155/) (2020) - [Crowdsourcing and Validating Event-focused Emotion Corpora for German and English](https://mlsanity.com/paper/1905.13618/) (2019) - [Dimensional Emotion Detection from Categorical Emotion](https://mlsanity.com/paper/1911.02499/) (2019) - [PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry](https://mlsanity.com/paper/2003.07723/) (2020) - [Towards Label-Agnostic Emotion Embeddings](https://mlsanity.com/paper/2012.00190/) (2020) - [APPReddit: a Corpus of Reddit Posts Annotated for Appraisal](https://mlsanity.com/paper/2205.15627/) (2022) - [Emotion Stimulus Detection in German News Headlines](https://mlsanity.com/paper/2107.12920/) (2021) ## Built on by (most cited first, 13 of 13) - [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024) - [Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks](https://mlsanity.com/paper/2405.05135/) (2024) - [Which Demographics do LLMs Default to During Annotation?](https://mlsanity.com/paper/2410.08820/) (2024) - [Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats](https://mlsanity.com/paper/2211.00171/) (2022) - [Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches](https://mlsanity.com/paper/2309.02092/) (2023) - [Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation](https://mlsanity.com/paper/2307.12973/) (2023) - [Emotion-Conditioned Text Generation through Automatic Prompt Optimization](https://mlsanity.com/paper/2308.04857/) (2023) - ["You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling](https://mlsanity.com/paper/2403.17612/) (2024) - [AMAQA: A Metadata-based QA Dataset for RAG Systems](https://mlsanity.com/paper/2505.13557/) (2025) - [Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks](https://mlsanity.com/paper/2312.08726/) (2023) - [MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification](https://mlsanity.com/paper/2306.08892/) (2023) - [Social Convos: Capturing Agendas and Emotions on Social Media](https://mlsanity.com/paper/2402.15571/) (2024) - [Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition](https://mlsanity.com/paper/2505.18040/) (2025) ## Abstract (arXiv) Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development. This conflicts with the classical paradigm of supervised learning in which the labels need to be predefined. A solution to obtain a model with a flexible set of labels is to use the paradigm of zero-shot learning as a natural language inference task, which in addition adds the advantage of not needing any labeled training data. This raises the question how to prompt a natural language inference model for zero-shot learning emotion classification. Options for prompt formulations include the emotion name anger alone or the statement "This text expresses anger". With this paper, we analyze how sensitive a natural language inference-based zero-shot-learning classifier is to such changes to the prompt under consideration of the corpus: How carefully does the prompt need to be selected? We perform experiments on an established set of emotion datasets presenting different language registers according to different sources (tweets, events, blogs) with three natural language inference models and show that indeed the choice of a particular prompt formulation needs to fit to the corpus. We show that this challenge can be tackled with combinations of multiple prompts. Such ensemble is more robust across corpora than individual prompts and shows nearly the same performance as the individual best prompt for a particular corpus.