We introduce KASHAF, to our knowledge the first interpretable Semantic Textual Similarity (iSTS) resource and end-to-end system for Arabic: a corpus of 7,811 human-annotated sentence pairs (inter-annotator κ=0.83κ= 0.83 κ=0.83 on a doubly-annotated subset) under a three-level hierarchical schema (sentence, phrase, and word), together with a modular pipeline that produces this layered analysis plus a composite similarity score, the KASHAF Hierarchical Similarity Score (KHSS), calibrated to the ordinal SEM scale. On a held-out cross-batch test split (1,002 pairs, disjoint from training and dev), KASHAF reaches macro-F1 =0.659= 0.659 =0.659 (±0.016±0.016 ±0.016, 5 seeds) and Spearman ρ=0.6715ρ= 0.6715 ρ=0.6715; a rule-based antonym/negation polarity gate provides an empirical score-capping rule for high-similarity contradictions, and the MARBERTv2 backbone leads three alternative Arabic encoders. As an off-the-shelf diagnostic rather than a fine-tuned comparison, four contemporary LLMs under matched prompting reach at most macro-F1 0.215 (standard prompting) and 0.336 (chain-of-thought), a 32–44-point gap that reflects systematic over-prediction of SEM-PARA at the expense of the dominant SEM-PARTIAL class. We release the schema, guidelines, code, models, and a reproducibility pipeline; the corpus is available from KSAA on request.

Linguistic Code Switching (CS) is a phenomenon that occurs when multilingual speakers alternate between two or more languages/dialects within a single conversation. Processing CS data is especially challenging in intra-sentential data given state-of-the-art monolingual NLP technologies since such technologies are geared toward the processing of one language at a time. In this paper, we address the problem of Part-of-Speech tagging (POS) in the context of linguistic code switching (CS). We explore leveraging multiple neural network architectures to measure the impact of different pre-trained embeddings methods on POS tagging CS data. We investigate the landscape in four CS language pairs, Spanish-English, Hindi-English, Modern Standard Arabic- Egyptian Arabic dialect (MSA-EGY), and Modern Standard Arabic- Levantine Arabic dialect (MSA-LEV). Our results show that multilingual embedding (e.g., MSA-EGY and MSA-LEV) helps closely related languages (EGY/LEV) but adds noise to the languages that are distant (SPA/HIN). Finally, we show that our proposed models outperform state-of-the-art CS taggers for MSA-EGY language pair.

In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.

We present our effort to create a large Multi-Layered representational repository of Linguistic Code-Switched Arabic data. The process involves developing clear annotation standards and Guidelines, streamlining the annotation process, and implementing quality control measures. We used two main protocols for annotation: in-lab gold annotations and crowd sourcing annotations. We developed a web-based annotation tool to facilitate the management of the annotation process. The current version of the repository contains a total of 886,252 tokens that are tagged into one of sixteen code-switching tags. The data exhibits code switching between Modern Standard Arabic and Egyptian Dialectal Arabic representing three data genres: Tweets, commentaries, and discussion fora. The overall Inter-Annotator Agreement is 93.1%.