DialSeg-Ar

Dataset Summary

DialSeg-Ar is a multi-genre benchmark for linear semantic segmentation in Arabic, with a focus on dialectal conversational and transcribed speech. The dataset is designed to evaluate how well models can split a sequence of utterances into contiguous topic-coherent segments.

The benchmark covers diverse and underrepresented Arabic genres, including:

• dialectal telephone conversations
• code-switched Gulf Arabic–English podcasts
• dialectal emotional dialogue from novels
• Modern Standard Arabic (MSA) news commentary

DialSeg-Ar is intended for research on:

• linear text segmentation
• discourse analysis
• conversational structure modeling
• segmentation for retrieval, summarization, and downstream discourse tasks
• low-resource and dialectal Arabic NLP

This dataset was introduced in:

Chirkunov, K., Samih, Y., Freihat, A. A., & Aldarmaki, H. (2026). Linear Semantic Segmentation for Low-Resource Spoken Dialects. In Proceedings of ACL 2026.

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Supported Tasks and Leaderboards

DialSeg-Ar supports the task of linear semantic segmentation, also known as linear text segmentation.

Given an ordered sequence of utterances, the goal is to predict the positions where a new topic segment begins.

Typical evaluation metrics include:

• Boundary F1
• Pk
• WindowDiff

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Languages

The dataset primarily contains Arabic, including:

• Modern Standard Arabic (MSA)
• Moroccan Arabic
• Gulf Arabic
• Levantine Arabic
• Iraqi Arabic

Some subsets also include English code-switching, especially in the podcast portion.

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Dataset Structure

Each row corresponds to one JSONL filename and contains:

• subset (string): subset name (for example ldc_gulf, mgb-5, rewayat).
• arabic_dialect (string): language label.
• genre (string): genre label (for example phone conversations, spontaneous speech, shows, drama).
• file_name (string): matched JSONL file name.
• lines (string): input text content in jsonl format.
• segmentation (string): text segments in jsonl format.