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Meta4XNLI: A Parallel Corpus for Multilingual Metaphor Detection and Interpretation


Core Concepts
Meta4XNLI is a parallel corpus in Spanish and English that provides metaphor annotations for both detection at the token level and interpretation through Natural Language Inference.
Abstract
Meta4XNLI is a novel parallel dataset that addresses the lack of multilingual resources for metaphor processing. It combines existing NLI datasets (XNLI and esXNLI) and enriches them with metaphor annotations in Spanish and English. The key highlights of the dataset are: Parallel data with metaphorical annotations at both the token level for detection and the premise-hypothesis pair level for interpretation. Enables cross-lingual analysis of metaphor by providing translations in both directions (EN->ES, ES->EN). Contains natural language sentences from multiple domains, rather than synthetically generated examples. Provides a sufficient number of instances to support comprehensive evaluation of metaphor processing capabilities in language models. The authors developed the annotations following the MIPVU guidelines for metaphor identification. For detection, they first automatically labeled the data using pre-trained models and then manually reviewed the predictions. For interpretation, they manually annotated premise-hypothesis pairs where understanding the literal meaning of metaphors is crucial to determine the inference relationship. The resulting dataset contains 13,320 sentences, with 14% of them having at least one metaphorical expression in Spanish and 20.5% in English. For the interpretation task, 12% of the premise-hypothesis pairs require understanding the literal meaning of metaphors to infer the relationship between them. This resource aims to facilitate research on multilingual and cross-lingual metaphor processing, enabling the exploration of metaphor transferability between languages and the impact of translation on the development of annotated datasets.
Stats
There are 1,155 metaphorical tokens in the Spanish premises and 1,139 in the hypotheses. 1,873 out of the 13,320 sentences (14%) in the Spanish dataset contain at least one metaphorical expression. There are 3,330 metaphorical tokens in the English dataset, with 2,736 out of 13,320 sentences (20.5%) containing at least one metaphorical expression. 12% of the premise-hypothesis pairs in the dataset require understanding the literal meaning of metaphors to determine the inference relationship.
Quotes
"Metaphors, although occasionally unperceived, are ubiquitous in our everyday language. Thus, it is crucial for Language Models to be able to grasp the underlying meaning of this kind of figurative language." "Meta4XNLI is a compilation of XNLI (Conneau et al. 2018) and esXNLI (Artetxe, Labaka, and Agirre 2020). We decided to exploit these resources since we evaluate metaphor interpretation through NLI and these datasets were originally developed for this task."

Key Insights Distilled From

by Elisa Sanche... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07053.pdf
Meta4XNLI

Deeper Inquiries

How can the insights from Meta4XNLI be leveraged to improve the performance of multilingual language models on tasks involving metaphorical language?

Meta4XNLI provides a valuable resource for improving the performance of multilingual language models on tasks involving metaphorical language. By offering a parallel dataset with metaphor annotations in both Spanish and English, it enables researchers to explore the transferability of metaphor knowledge across languages. This can help in training and fine-tuning multilingual language models to better understand and process metaphors in different languages. The dataset allows for cross-lingual analysis of metaphors, enabling researchers to study how metaphors are expressed and interpreted in different linguistic contexts. By leveraging Meta4XNLI, researchers can develop and evaluate multilingual language models that are more adept at handling metaphorical language across various languages.

What are the potential challenges in transferring metaphor knowledge across languages, and how can they be addressed?

Transferring metaphor knowledge across languages poses several challenges. One major challenge is the cultural and linguistic differences that exist between languages, leading to variations in how metaphors are used and understood. Additionally, some metaphors may be unique to specific languages or cultures, making direct translation challenging. To address these challenges, researchers can employ techniques such as parallel corpus analysis to identify equivalent metaphors in different languages. They can also utilize cross-lingual language models that have been trained on multilingual data to capture the nuances of metaphorical language across languages. Furthermore, incorporating cultural and contextual information into the training data can help improve the transferability of metaphor knowledge across languages.

How might the understanding of metaphors in natural language be connected to broader questions of human cognition and the relationship between language and thought?

The understanding of metaphors in natural language is closely connected to broader questions of human cognition and the relationship between language and thought. Metaphors play a crucial role in how we conceptualize and communicate abstract ideas, emotions, and experiences. The ability to understand and interpret metaphors involves complex cognitive processes, including metaphorical mapping, inference, and abstraction. Studying how individuals perceive and interpret metaphors can provide insights into how language shapes our thinking, reasoning, and perception of the world around us. It can also shed light on the role of figurative language in cognitive development, creativity, and communication. By exploring the cognitive mechanisms underlying metaphor comprehension, researchers can gain a deeper understanding of the intricate relationship between language, thought, and human cognition.
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