# Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation

arXiv: https://arxiv.org/abs/2404.07053 · HTML: https://mlsanity.com/paper/2404.07053/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 97 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [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)
- [Stress Test Evaluation for Natural Language Inference](https://mlsanity.com/paper/1806.00692/) (2018)
- [Translation Artifacts in Cross-lingual Transfer Learning](https://mlsanity.com/paper/2004.04721/) (2020)
- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [Testing the Ability of Language Models to Interpret Figurative Language](https://mlsanity.com/paper/2204.12632/) (2022)
- [Multi-lingual and Multi-cultural Figurative Language Understanding](https://mlsanity.com/paper/2305.16171/) (2023)
- [MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories](https://mlsanity.com/paper/2104.13615/) (2021)
- [Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages](https://mlsanity.com/paper/2203.14139/) (2022)
- [Figurative Language in Recognizing Textual Entailment](https://mlsanity.com/paper/2106.01195/) (2021)
- [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022)
- [FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning](https://mlsanity.com/paper/2302.04834/) (2023)
- [Metaphor Detection with Effective Context Denoising](https://mlsanity.com/paper/2302.05611/) (2023)
- [Metaphor Detection via Explicit Basic Meanings Modelling](https://mlsanity.com/paper/2305.17268/) (2023)
- [A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection](https://mlsanity.com/paper/2305.17480/) (2023)
- [MiQA: A Benchmark for Inference on Metaphorical Questions](https://mlsanity.com/paper/2210.07993/) (2022)
- [Adversarial Multi-task Learning for End-to-end Metaphor Detection](https://mlsanity.com/paper/2305.16638/) (2023)
- [Metaphorical Polysemy Detection: Conventional Metaphor meets Word Sense Disambiguation](https://mlsanity.com/paper/2212.08395/) (2022)
- [Leveraging a New Spanish Corpus for Multilingual and Crosslingual Metaphor Detection](https://mlsanity.com/paper/2210.10358/) (2022)
- [Interpreting Verbal Metaphors by Paraphrasing](https://mlsanity.com/paper/2104.03391/) (2021)
- [How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection](https://mlsanity.com/paper/2104.03928/) (2021)
- [Does the "most sinfully decadent cake ever" taste good? Answering Yes/No Questions from Figurative Contexts](https://mlsanity.com/paper/2309.13748/) (2023)

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

- [Lost in Variation? Evaluating NLI Performance in Basque and Spanish Geographical Variants](https://mlsanity.com/paper/2506.15239/) (2025)
- [La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America](https://mlsanity.com/paper/2507.00999/) (2025)
- [CAMMAR: Culture-Aware Matryoshka for Metaphorical Arabic Representations](https://mlsanity.com/paper/2607.15847/) (2026)
- [Figurative Justice: Detecting metaphors in Hindi judgements with qualitative assessment and transformers](https://mlsanity.com/paper/2608.22446/) (2026)
- [Cross-Dataset Stability of Expert-Informed Skill Prompting and Fine-Tuning for Chinese Metaphor Identification](https://mlsanity.com/paper/2608.25579/) (2026)

## Abstract (arXiv)

Metaphors are a ubiquitous but often overlooked part of everyday language. As a complex cognitive-linguistic phenomenon, they provide a valuable means to evaluate whether language models can capture deeper aspects of meaning, including semantic, pragmatic, and cultural context. In this work, we present Meta4XNLI, the first parallel dataset for Natural Language Inference (NLI) newly annotated for metaphor detection and interpretation in both English and Spanish. Meta4XNLI facilitates the comparison of encoder- and decoder-based models in detecting and understanding metaphorical language in multilingual and cross-lingual settings. Our results show that fine-tuned encoders outperform decoders-only LLMs in metaphor detection. Metaphor interpretation is evaluated via the NLI framework with comparable performance of masked and autoregressive models, which notably decreases when the inference is affected by metaphorical language. Our study also finds that translation plays an important role in the preservation or loss of metaphors across languages, introducing shifts that might impact metaphor occurrence and model performance. These findings underscore the importance of resources like Meta4XNLI for advancing the analysis of the capabilities of language models and improving our understanding of metaphor processing across languages. Furthermore, the dataset offers previously unavailable opportunities to investigate metaphor interpretation, cross-lingual metaphor transferability, and the impact of translation on the development of multilingual annotated resources.