Morocco

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.

EACL 2026

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

authorial style transfer

computational stylistics

turkish

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ACL 2023

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workshop paper

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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. 

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.

Effects of Speaker Bias in Dialect Identification and Automatic Transcription with Self-Supervised Speech Models

Mutual intelligibility within language families presents a significant challenge for multilingual NLP, particularly due to the prevalence of dialectal variation and asymmetric comprehension. In this paper, we present a corpus-based computational analysis to quantify linguistic proximity across Romance language variants, with a focus on major Spanish (Argentine, Chilean and European) and Portuguese (Brazilian and European) varieties and the other main Romance languages (Italian, French, Romanian). We introduce a novel computational metric of lexical intelligibility based on surface and semantic similarity of related words, and use it to measure mutual intelligibility for the five main Romance languages in relation to the Spanish and Portuguese varieties studied.

On the Intelligibility of Romance Language Varieties: Spanish and Portuguese in Europe and America

We conduct an empirical study of cross-lingual transfer using spontaneous, noisy, and code-mixed speech across a wide range of Indic dialects and language varieties. Our results indicate that although ASR performance is generally associated with phylogenetic distance across languages, this factor alone does not fully explain performance in dialectal settings. Often, fine-tuning on smaller amounts of dialectal data yields performance comparable to fine-tuning on larger amounts of phylogenetically-related, high-resource standardized languages. We also present a case study on Garhwali, a low-resource Pahari language variety, and evaluate multiple contemporary ASR models. Finally, we analyze transcription errors to examine bias toward pre-training languages, providing additional insight into challenges faced by ASR systems on dialectal and non-standardized speech.

Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties

This paper addresses key gaps in assessing Dialectal Arabic to Modern Standard Arabic (DA-MSA) translation quality, where standard automatic metrics and general-purpose human evaluation methods fail to capture dialect-specific errors. We introduce Ara-HOPE, a human-centric post-editing evaluation framework comprising a five-category error taxonomy and a decision-tree annotation protocol designed for consistent use by minimally trained native dialect speakers. Using Ara-HOPE, we comparatively evaluate three mainstream systems (Jais, GPT-3.5, and NLLB-200) to quantify error patterns, pinpoint systematic weaknesses, and provide actionable guidance for improving dialect-aware MT. Our analysis shows clear performance differences across systems and highlights dialect-specific terminology handling and semantic preservation as the most persistent challenges in DA-MSA translation.

Ara-HOPE: Human-Centric Post-Editing Evaluation for Dialectal Arabic to Modern Standard Arabic Translation

Multilingual large language models (LLMs) are increasingly deployed in linguistically diverse regions like India, yet most interpretability tools remain tailored to English. Prior work reveals that LLMs often operate in English centric representation spaces, making cross lingual interpretability a pressing concern. We introduce Indic-TunedLens, a novel interpretability framework specifically for Indian languages that learns shared affine transformations. Unlike the standard Logit Lens, which directly decodes intermediate activations, Indic-TunedLens adjusts hidden states for each target language, aligning them with the target output distributions to enable more faithful decoding of model representations. We evaluate our framework on 10 Indian languages using the MMLU benchmark and find that it significantly improves over SOTA interpretability methods, especially for morphologically rich, low resource languages. Our results provide crucial insights into the layer-wise semantic encoding of multilingual transformers. Our model is available at https://huggingface.co/spaces/AnonymousAccountACL/IndicTunedLens. Our code is available at https://github.com/AnonymousAccountACL/IndicTunedLens.

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Tokenisation of Turkic Copula Constructions in Universal Dependencies

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Balancing the Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer

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