# Linear Semantic Segmentation for Low-Resource Spoken Dialects

arXiv: https://arxiv.org/abs/2605.06276 · HTML: https://mlsanity.com/paper/2605.06276/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 22 of 22)

- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [EmbeddingGemma: Powerful and Lightweight Text Representations](https://mlsanity.com/paper/2509.20354/) (2025)
- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [ALLaM: Large Language Models for Arabic and English](https://mlsanity.com/paper/2407.15390/) (2024)
- [Text Segmentation by Cross Segment Attention](https://mlsanity.com/paper/2004.14535/) (2020)
- [Topic-Aware Multi-turn Dialogue Modeling](https://mlsanity.com/paper/2009.12539/) (2020)
- [A Panoramic Survey of Natural Language Processing in the Arab World](https://mlsanity.com/paper/2011.12631/) (2020)
- [Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation](https://mlsanity.com/paper/2001.00891/) (2020)
- [Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation](https://mlsanity.com/paper/2406.16678/) (2024)
- [Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study](https://mlsanity.com/paper/2305.08391/) (2023)
- [From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions](https://mlsanity.com/paper/2402.17633/) (2024)
- [NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities](https://mlsanity.com/paper/2505.18383/) (2025)
- [Mixat: A Data Set of Bilingual Emirati-English Speech](https://mlsanity.com/paper/2405.02578/) (2024)
- [Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks](https://mlsanity.com/paper/2411.01192/) (2024)
- [Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning](https://mlsanity.com/paper/2407.21139/) (2024)
- [Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts](https://mlsanity.com/paper/2507.04569/) (2025)
- [Recent Trends in Linear Text Segmentation: a Survey](https://mlsanity.com/paper/2411.16613/) (2024)
- [Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats](https://mlsanity.com/paper/2211.14954/) (2022)
- [CoCo-CoLa: Evaluating and Improving Language Adherence in Multilingual LLMs](https://mlsanity.com/paper/2502.12476/) (2025)
- [Variation and Instability in Dialect-Based Embedding Spaces](https://mlsanity.com/paper/2303.14963/) (2023)
- [GemMaroc: Unlocking Darija Proficiency in LLMs with Minimal Data](https://mlsanity.com/paper/2505.17082/) (2025)

## Built on by (most cited first, 1 of 1)

- [Arabic Sentence Segmentation Across Genres and Punctuation Conditions](https://mlsanity.com/paper/2606.08025/) (2026)

## Abstract (arXiv)

Semantic segmentation is a core component of discourse analysis, yet existing models are primarily developed and evaluated on high-resource written text, limiting their effectiveness on low-resource spoken varieties. In particular, dialectal Arabic exhibits informal syntax, code-switching, and weakly marked discourse structure that challenge standard segmentation approaches. In this paper, we introduce a new multi-genre benchmark (more than 1000 samples) for semantic segmentation in conversational Arabic, focusing on dialectal discourse. The benchmark covers transcribed casual telephone conversations, code-switched podcasts, broadcast news, and expressive dialogue from novels, and was annotated and validated by native Arabic annotators. Using this benchmark, we show that segmentation models performing well on MSA news genres degrade on dialectal transcribed speech. We further propose a segmentation model that targets local semantic coherence and robustness to discourse discontinuities, consistently outperforming strong baselines on dialectal non-news genres. The benchmark and approach generalize to other low-resource spoken languages.