Morocco

Idiomatic expressions are culturally grounded, semantically opaque, and difficult to interpret for multilingual natural language processing systems. Despite the large speaker population of Turkic languages, resources that focus on monolingual and cross-lingual idioms and their meanings are limited. We introduce the first unified benchmark for idiom understanding across Turkish, Azerbaijani, Turkmen, Gagauz, and Uzbek languages. The datasets compiled include token-level idiom span annotations. We develop models for idiom identification and semantic retrieval tasks. We evaluate seven models for idiom identification and nine embedding models for semantic retrieval tasks under several fine-tuning schemes using standard dense retrieval metrics. This benchmark provides a basis for studying idiomatic phenomena in Turkic languages and clarifies how idiomatic meanings are shared, altered, or diverge across languages.

EACL 2026

A Unified Turkic Idiom Understanding Benchmark: Idiom Detection and Semantic Retrieval Across Five Turkic Languages

idiom understanding

idiom detection

turkic languages

## Welcome to EMNLP 2022!
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#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

We present, to our knowledge, the first systematic transformer-based outlet-ideology classification study for Turkish news. Using a topic-balanced corpus of Turkish political articles drawn from six outlets commonly perceived as left-, centre-, or right-leaning, we formulate a three-way outlet-ideology classification task. On this dataset, we evaluate a monolingual encoder (BERTurk), two multilingual encoders (mBERT, XLM-R), and a LoRA-adapted decoder model (Mistral). BERTurk achieves the best performance among individual models (70% accuracy, 71% macro-F1), reaching levels comparable to English-language studies despite operating in a lower-resource setting. Error analyses show that all encoders reliably distinguish centrist from partisan articles, but frequently confuse left- and right-leaning articles with each other. Moreover, BERTurk is relatively stronger on right-leaning content, whereas the multilingual models favour left-leaning content, suggesting an “ideological fingerprint” of their pre-training data. Crucially, models fine-tuned on an English political-bias task fail to transfer to Turkish, collapsing to near-chance performance. Taken together, these results demonstrate that effective political bias detection requires target-language supervision and cannot be achieved through naïve cross-lingual transfer. Our work establishes a first baseline for Turkish political bias detection and underscores the need for open, carefully designed Turkish (and broader Turkic) bias benchmarks to support robust and fair media analysis.

Language Matters: Target-Language Supervision for Political Bias Detection in Turkish News

In this paper, we investigated the task of hate-speech classification in the closely related Turkic language pair, Turkish-Azerbaijani. Transformer models can achieve strong hate-speech classification in Turkish, but their performance does not reliably transfer to closely related low-resource languages without careful evaluation. We study Turkish–Azerbaijani hate speech detection and introduce the first manually annotated Azerbaijani benchmark, comprising 1,112 YouTube comments from major news channels with severe class imbalance. We compare XLM-RoBERTa and a compact BERT-Tiny model against a TF–IDF + logistic regression baseline under monolingual training, zero-shot Turkish→Azerbaijani transfer, low-resource balanced subsampling, bilingual mixed fine-tuning, and translation-based augmentation using machine-translated Turkish data. XLM-R attains high macro-F1 in Turkish and achieves moderate zero-shot transfer to Azerbaijani, but native Azerbaijani training is fragile for the hate class. Mixed bilingual training improves robustness for both languages, whereas TF–IDF generalizes poorly to Azerbaijani.

Benchmarking Hate Speech Detection in Azerbaijani with Turkish Cross-Lingual Transfer and Transformer Models

From Lemmas to Dependencies: What Signals Drive Light Verbs Classification?

Authorial style transfer is particularly challenging in low-resource scenarios, such as those presented by languages with a distinct socio-digital trajectory like Turkish, where contemporary digital text coexists with under-resourced literary and historical styles. This work addresses this gap through the Dual-Stage Stylometric Imprinting (DSSI) framework, introducing a Rule+Example paradigm for effective style profiling. Evaluated on a corpus of Turkish texts, the approach enables smaller models to achieve up to 90% of large model performance by combining explicit stylistic guidelines with contextual demonstrations. The findings demonstrate altered scaling laws for stylistic tasks and facilitate the practical deployment of personalized style transfer for preserving distinctive writing characteristics.

Directed Attention is All You Need: Profiling Style from Limited Text Data

Identifying units, ’syntactic words’, for morphosyntactic analysis is important yet challenging for morphologically rich languages. In this paper we propose a set of guiding principles to determine units of morphosyntactic analysis, and apply them to the case of copular constructions in Turkic languages, in the context of Universal Dependencies (UD) framework. We also provide a survey of the practice in the Turkic UD treebanks published to date, and discuss the advantages and disadvantages of the proposed tokenisation for a selection of Turkic languages.

Tokenisation of Turkic Copula Constructions in Universal Dependencies

The Iranic language family includes many underrepresented languages and dialects that remain largely unexplored in modern NLP research. We introduce APARSIN, a multi-variety benchmark covering 14 Iranic languages, dialects, and accents, designed for sentiment analysis and machine translation. The dataset includes both highand low-resource varieties, several of which are endangered, capturing linguistic variation across them. We evaluate a set of instruction-tuned Large Language Models (LLMs) on these tasks and analyze their performance across the varieties. Our results highlight substantial performance gaps between standard Persian and other Iranic languages and dialects, demonstrating the need for more inclusive multilingual and dialectally diverse NLP benchmarks.

APARSIN: A Multi-Variety Sentiment and Translation Benchmark for Iranic Languages

Despite recent advances in automatic web register (genre) labeling and its applications to web-scale datasets and LLM development, the effectiveness of these tools for digitally lowresource languages remains unclear. This study introduces ParsCORE, the first largescale collection of Persian web registers (genres), and evaluates deep learning models for register classification and keyword analysis across major registers. Using 2,000 humanannotated documents, the models achieved a micro F1-score of 0.76. The findings provide a foundation for future research on the linguistic and cultural specificities of Persian registers.

ParsCORE: The Persian Corpus of Online Registers

The Iranian linguistic family is pluricentric, encompassing Iranian Persian, Dari (Afghanistan), and Tajiki (Tajikistan). While Multilingual Large Language Models (MLLMs) claim broad coverage, their robustness across these regional variants and script differences (Perso-Arabic vs. Cyrillic) remains under-explored, particularly in the open-weight landscape. We evaluate five openweight models from the Qwen, Bloomz, and Gemma families across four downstream tasks: Sentiment Analysis, Machine Translation (MT), NLI, and QA. Utilizing a dataset of over 240,000 processed samples, we observe severe performance disparities. While the fine-tuned gemma-3-4b-persian achieves promising results on Iranian Persian (77.3% accuracy in Sentiment), almost all tested models appear to suffer catastrophic degradation on Tajiki script (dropping to 1.0 BLEU). These findings highlight a critical “script barrier” in current open-weight MLLM development for Central Asian languages. Code and data available here.

One Language, Three of Its Voices: Evaluating Multilingual LLMs Across Persian, Dari, and Tajiki

We investigate structural traces of language contact in the intermediate representations of a monolingual language model. Focusing on Persian (Farsi) as a historically contact-rich language, we probe the representations of a Persian-trained model when exposed to languages with varying degrees and types of contact with Persian. Our methodology quantifies the amount of linguistic information encoded in intermediate representations and assesses how this information is distributed across model components for different morphosyntactic features. The results show that universal syntactic information is largely insensitive to historical contact, whereas morphological features such as CASE and GENDER are strongly shaped by language-specific structure, suggesting that contact effects in monolingual language models are selective and structurally constrained.

A Computational Approach to Language Contact – A Case Study of Persian

A major issue in audio modeling is speaker bias, in which the models learn language external traits, such as a speaker's timbre or pitch, and use this information as a short-cut to a language task. This is especially problematic for dialectology, as it is typical in dialect corpora that only a few speakers represent a complete dialect area. In this paper, we explore the effects of speaker bias in two dialectal tasks: dialect identification and automatic dialectal transcription. We build two different data partitions of dialect interviews in Finnish and Norwegian: 1) a speaker dependent partition in which all of the speakers appear in training, development, and test sets, and 2) a speaker independent partition where each speaker only appears in exactly one set. We further experiment with modifications of the training data by augmenting the original audio with pitch shifts and noise, as well as changing the original speakers' voices with voice conversion models. We show that the dialect identification models are highly effected by speaker bias, whereas automatic dialectal transcription models are not. The audio modifications do not offer major performance gains for either of the languages or tasks.

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Next from EACL 2026

Language Matters: Target-Language Supervision for Political Bias Detection in Turkish News

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