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README.md
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license: cc-by-nc-sa-4.0
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---
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---
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license: cc-by-nc-sa-4.0
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+
task_categories:
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- text-classification
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- translation
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language:
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- en
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- es
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pretty_name: IdioTS - Idiomatic Language Test Suite
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size_categories:
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- n<1K
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---
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# Dataset Card for IdioTS - Idiomatic Language Test Suite
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The first version of this evaluation dataset was created as part of a Master's thesis in NLP
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under the title "Idiom detection and translation with conversational LLMs".
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The dataset has been further curated and improved and is constantly revised by the author.
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## Dataset Details
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### Dataset Description
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<!-- Provide a longer summary of what this dataset is. -->
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- **Curated by:** Francesca De Luca Fornaciari
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** cc-by-nc-sa-4.0
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### Dataset Sources [optional]
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<!-- Provide the basic links for the dataset. -->
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- **Repository:** https://github.com/fdelucaf/idioms-experiments
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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This dataset is designed for the assessment of conversational LLMs' capabilities to process figurative language,
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specifically idiomatic expressions at sentence level.
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### Direct Use
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This dataset can be used for the assessment of conversational LLMs on two tasks related with idiomatic language:
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Task 1 (monolingual task): idiom detection in an English sentence.
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Task 2 (cross-lingual task): sentence translation from English to Spanish.
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### Out-of-Scope Use
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This dataset is not meant to be used for tasks that differ from the ones specified in "Direct Use".
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## Dataset Structure
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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This evaluation dataset was designed and curated by human experts with linguistic knowledge, specifically to assess the ability of
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LLMs to process figurative language at sentence level.
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With the release of this dataset, we aim to provide a resource for evaluating the capabilities of conversational LLMs
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to handle the semantic meanings of multi-word expressions and to distinguish between literal
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and idiomatic meanings of a potentially figurative expression (PIE).
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### Source Data
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The sentence dataset is based on an original list of English idioms. This list was curated by the same author as the dataset.
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The original English idioms are partly derived from real interactions of the author with native English speakers
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and partly extracted from the following websites:
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[Amigos Ingleses}](https://www.amigosingleses.com/),
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[The idioms](https://www.theidioms.com/),
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[EF English idioms](https://www.ef.com/wwen/english-resources/english-idioms/).
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#### Data Collection and Processing
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The original English sentences in the dataset were crafted by a group of native English speakers
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in the frame of a small-scale crowdsourcing on voluntary basis.
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#### Who are the source data producers?
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In order to ensure the quality of the generated sentences, the selected collaborators had to fulfil the following requirements:
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- Native English speakers, predominantly of British origins.
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- Demonstrated high linguistic proficiency attaining at least a C1 level.
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- Language professional profile with a linguistic background (English teachers, linguists, translators, and NLP experts).
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### Annotations [optional]
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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#### Annotation process
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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[More Information Needed]
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#### Who are the annotators?
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<!-- This section describes the people or systems who created the annotations. -->
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[More Information Needed]
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#### Personal and Sensitive Information
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The dataset does not contain any kind of personal or sensitive information.
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## Bias, Risks, and Limitations
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A concerted effort was made to mitigate gender bias within our newly developed resource.
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Whenever possible, gender-specific terms were either eliminated or neutralised, a large number of sentences
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were reformulated adopting a gender neutral first person plural (``we''/``us''), second person singular or
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plural (``you''), or third person plural (``they''). Since the gender neutralisation is not always possible
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due to grammatical or syntactical constraints, meticulous attention was devoted to ensuring a representation
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of feminine and masculine gender terms as balanced as possible throughout the dataset.
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No specific measures were taken to mitigate other types of bias that may be present in the data.
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### Recommendations
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Dataset Card Authors [optional]
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[More Information Needed]
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## Dataset Card Contact
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f.delucafornaciari@gmail.com
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