@Book{NeTTIT:2026, editor = {Marie Escribe and Alicia Picazo Izquierdo and Constantin Orăsan and Tharindu Ranasinghe and Gloria Corpas Pastor and Marko Tadić and Ruslan Mitkov}, title = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, url = {https://aclanthology.org/2026.nettit-1} } @InProceedings{farrell:2026:NeTTIT, author = {Farrell, Michael}, title = {Do readers prefer AI-generated Italian short stories?}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {1--8}, abstract = {This study investigates whether readers prefer AI-generated short stories in Italian over one written by a renowned Italian author. In a blind setup, 20 participants read and evaluated three stories, two created with ChatGPT-4o and one by Alberto Moravia, without being informed of their origin. To explore potential influencing factors, reading habits and demographic data, comprising age, gender, education and first language, were also collected. The results showed that the AI-written texts received slightly higher average ratings and were more frequently preferred, although differences were modest. No statistically significant associations were found between text preference and demographic or reading-habit variables. These findings challenge assumptions about reader preference for human-authored fiction and raise questions about the necessity of synthetic-text editing in literary contexts.}, url = {https://aclanthology.org/2026.nettit-1.1} } Author{1}{Orcid}:https://orcid.org/0000-0002-7138-6639 @InProceedings{julaiti-cheung:2026:NeTTIT, author = {Julaiti, Kaifusai and Cheung, Andrew K. F.}, title = {A Corpus-based Analysis of Semantic Similarity in Machine Translation and Human Interpreting}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {68--78}, abstract = {The incorporation of machine translation (MT) in interpreting activities has drawn increasing research attention. However, MT has often been examined as an assistant to interpreters within the framework of computer- or AI-assisted interpreting. Its independent performance in rendering original meaning in interpreting context, particularly in comparison to interpreter performance, has been relatively unclear. Within the Chinese-English language pair, this study examines the performance of MT systems and simultaneous interpreters in conveying semantic meaning, measuring it through semantic similarity using LaBSE, COMET, and partial human assessment. The findings suggest that though MT systems may deliver sufficient semantic meaning, they have yet to reach human parity in conveying implicit semantic cohesion and nuanced language use, which thus requires further fine-tuning.}, url = {https://aclanthology.org/2026.nettit-1.10} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{li:2026:NeTTIT, author = {Li, Ailin}, title = {Generated Voices: Examining Patterns and Shifts in AI Translation of Online Feminist Discourse}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {79--83}, abstract = {A decade of research into machine bias has illustrated how gendered assumptions are embedded in MT technology, yet how these systems handle gendered ideological discourse remains largely unexplored. By observing the performance of ChatGPT, Claude, DeepSeek and Qwen on the translation of feminist discourse, this study investigates the way LLMs mediate and rewrite gendered ideological texts. It aims to uncover the dynamics between gender power, bias and censorship in LLM translation while reflecting on the ideological consequences of the invisible technical design choices behind AI tools. Keywords: Large Language Models, Machine Bias, Machine Translation}, url = {https://aclanthology.org/2026.nettit-1.11} } Author{1}{Orcid}: @InProceedings{theuerkauf-EtAl:2026:NeTTIT, author = {Theuerkauf, Lukas and Neumann, Ksenia and Nahhas, Abdulrahman and Walia, Damanpreet Singh and Chernigovskaya, Maria}, title = {Challenges in Code translation using Large language models}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {84--92}, abstract = {This paper examines major challenges in code translation (CT) using Large Language Models (LLMs). Furthermore, it proposes potential so- lution approaches to address these translation challenges. The applied method is a systematic literature review (SLR) , which summarizes current knowledge from various experiments and investigates observable patterns. The re- sults indicate that code conversion between pro- gramming languages (PLs) can be significantly simplified by optimizing each step in the LLM workflow. The deployment of standardization frameworks plays a central role in this process. This study contributes to the existing literature by providing a structured overview of the chal- lenges in code translation, as well as offering several best-practice solutions to guide readers.}, url = {https://aclanthology.org/2026.nettit-1.12} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}: Author{4}{Orcid}: Author{5}{Orcid}: @InProceedings{he-shi:2026:NeTTIT, author = {HE, Sihui and SHI, Jiawei}, title = {Exploring Sentiment Transfer in Cantonese–English Court Interpreting: A Computational Analysis Using Multilingual BERT}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {93--97}, abstract = {The way sentiment is conveyed during court interpreting may result in different legal outcomes. This study used multilingual BERT to analyze the sentiment of the Hong Kong Court Interpreting Corpus (HKCI). The corpus comprises 101 audio recordings from five rape trials at the High Court of Hong Kong. The results showed that negative sentiment predominated across all four sub-corpora and all pairwise comparisons were statistically significant. Moreover, in the C2E direction, the negative sentiment was reduced, while in the E2C direction, increased negative sentiment was observed. This pattern may be partly explained by the structural differences between Cantonese and English. In addition, institutional power status was not found to predict sentiment shift in either direction. Furthermore, BERT predictions showed low agreement with human annotations (κ = 0.19). These findings may provide insights for court interpreting. The intensified negative sentiment in E2C output suggests witnesses and defendants may perceive more negative renditions of legal professionals' speech, which raises potential concerns for procedural justice. These findings also indicate the need to develop domain-adapted sentiment tools for low-resource legal discourse.}, url = {https://aclanthology.org/2026.nettit-1.13} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{wang-EtAl:2026:NeTTIT, author = {Wang, Lulu and Zhang, Hengtian and Macken, Lieve and Liu, Kanglong}, title = {How Decoding Settings Shape Lexical Variation in Iterative LLM-Translation Workflows: A Case Study of Chinese-English Literary Translation}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {98--108}, abstract = {Large language models (LLMs) are increasingly used in automated translation workflows involving iterative refinement, yet the role of decoding settings in shaping lexical variation within such workflows remains underexplored. This study examines how temperature and top-p settings influence lexical diversity, lexical frequency distribution, and inter-stage lexical similarity within a three-stage literary translation pipeline (initial translation, self-revision, and stylistic refinement). Using GPT-5.1, we translated contemporary Chinese fiction texts under controlled parameter settings. Results show that less restrictive decoding (higher temperature and top-p) leads to increased lexical diversity, higher proportions of low-frequency vocabulary, and more extensive inter-stage revision, with these effects amplified across successive refinement stages. Compared with professional human translations, LLM outputs exhibit higher lexical diversity and a preference for lower-frequency vocabulary across all conditions. The findings reveal how decoding parameters shape lexical variation in LLM-generated translations, with implications for LLM-based translation workflow design.}, url = {https://aclanthology.org/2026.nettit-1.14} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}: Author{4}{Orcid}: @InProceedings{gene-sosoni:2026:NeTTIT, author = {Gene, Viveta and Sosoni, Vilelmini}, title = {Dual-Metric Compliance and Quality Evaluation of Knowledge Graph Mediated Translation in Regulated Domains: An Enhanced Architectural Framework}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {109--118}, abstract = {This paper presents the second phase of an empirical research programme evaluating Knowledge Graph-Mediated Translation (KGMT) in the regulated dermocosmetics domain. Building on Gene and Sosoni (2026), which introduced KGMT as a theoretical framework for compliance-aware multilingual communication, Phase 2 advances the research through three coordinated contributions: an indexed source design with 30 controlled compliance errors drawn from EU and US regulatory frameworks; a Non-Compliance in Source (NCS) classification that principally distinguishes source-propagated from translation-introduced errors; and an enhanced KGMT pipeline incorporating a domain-specific style guide, a lemmatization layer, and an expanded multilingual glossary. Translations of a 500-word retinol product description into Greek and French are evaluated across three conditions —Pure Baseline (PB), Document-Augmented (DA), and KGMT— using the PE.MAPS dual-metric framework (Gene, 2024). KGMT achieves 100\% source compliance detection recall against 0\% for both baselines, and records zero penalized translation errors in both target languages under the NCS classification. Inter-condition divergence in baseline outputs from the same prompt on different inference instances demonstrates that LLMs do not reliably or auditably guarantee compliance consistency, positioning KGMT as a deterministic, knowledge-grounded stabilization layer.}, url = {https://aclanthology.org/2026.nettit-1.15} } Author{1}{Orcid}:0000-0002-4374-8305 Author{2}{Orcid}:0000-0002-9583-4651 @InProceedings{gene-cannavina:2026:NeTTIT, author = {Gene, Viveta and Cannavina, Valeria}, title = {From Diagnosis to Action: An Evidence Based Maturity Model for AI Integration in Translation}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {119--129}, abstract = {The evolution of Artificial Intelligence (AI) in the language services industry has shifted professional practice beyond traditional machine translation (MT) post-editing (PE) toward increasingly AI-enabled, hybrid localization workflows. While AI adoption has accelerated, existing maturity frameworks remain largely organization-centric and insufficiently address the evolving roles, skills, and ethical responsibilities of linguists and language service providers (LSPs). This study presents a data-driven AI Maturity Model (MM) designed specifically for the localization ecosystem, grounded in empirical evidence from a global survey of 400 professionals conducted in 2025, with longitudinal comparison to two parallel surveys conducted in 2020 (Gene, 2020) targeting linguists and LSP managers respectively, comprising linguists, LSP managers, and localization leaders. Using mixed-methods analysis, the study identifies patterns of AI integration, workflow adaptation, skill development, ethical readiness, and client alignment across regions and professional roles. Based on these findings, a four-stage AI maturity framework (Emerging, Developing, Strategic, and Transformative) is derived and subjected to preliminary external review through expert feedback gathered during industry webinars; formal empirical validation represents a direction for future research. The proposed model functions as an assessment and roadmap tool, enabling linguists and LSPs to evaluate their state of AI adoption and plan progression.}, url = {https://aclanthology.org/2026.nettit-1.16} } Author{1}{Orcid}:0000-0002-4374-8305 Author{2}{Orcid}: @InProceedings{shahnazari-alsulaiman:2026:NeTTIT, author = {Shahnazari, Masoud and Alsulaiman, Abied}, title = {Enhancing Persian-Arabic OCR for Translation Workflows: A Benchmark-Driven Optimization Approach}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {130--137}, abstract = {Optical Character Recognition (OCR) is a critical preprocessing stage for enabling machine translation (MT) and computer-assisted translation (CAT) workflows on Persian and Arabic printed and scanned documents. Within the translation industry in the Middle East, a substantial proportion of documents handled by translators are still received as scanned PDF files. This requires OCR as an unmissable preprocessing step before such documents can be integrated into MT and CAT tools. However, the cursive nature of these languages, positional letter variation, and dense diacritics limit recognition accuracy. We present a translation-oriented OCR pipeline for Persian and Arabic that combines systematic benchmarking of multiple open-source and vision-language OCR systems with targeted model adaptation. The framework integrates preprocessing, normalization, and structured post-processing, and further explores fine-tuning possibilities using LoRA to improve script-specific robustness. Through experimental evaluation, the optimized system achieves a mean Character Error Rate (CER) of 0.0224 for Persian and 0.0265 for Arabic, corresponding to character accuracies above 97\% and word accuracies of 89–90\%. Compared to baseline averages (CER ≈ 0.18–0.22), the improvement is substantial. The resulting workflow produces stable output for translation tools and consequently reduces manual correction effort in large-scale digitization and translation pipelines.}, url = {https://aclanthology.org/2026.nettit-1.17} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{noriega-corpaspastor:2026:NeTTIT, author = {Noriega, Laura and Corpas Pastor, Gloria}, title = {A Corpus-based Approach to Translating Specialised Terminology in Literature: A Two-step Protocol}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {138--145}, abstract = {In the current context of increasing reliance on artificial intelligence (AI) tools, other traditionally valuable resources, such as corpora, risk being underutilised in both translation practice and training. This pilot study proposes and empirically evaluates a corpus-based two-step protocol for translating specialised terms in literary texts in the English-Iberian Spanish language pair. The empirical analysis is based on four excerpts from contemporary novels, compiled into a small-scale dataset and analysed using five parallel corpora and two monolingual corpora. In the first phase, specialised terms are systematically extracted and translated using parallel corpus evidence. In the second phase, terms exhibiting polysemy or variation are analysed in monolingual corpora to refine candidate translations and assess their contextual adequacy. The protocol is evaluated through a combined qualitative error analysis and comparative assessment of translation choices across stages, focusing on terminological accuracy, contextual appropriateness, and consistency. The results indicate that the second phase contributes to measurable improvements in disambiguation and lexical selection, particularly in cases of domain-specific meaning extension in literary contexts. Overall, the study provides empirical evidence for the effectiveness of corpus-based workflows in terminological decision-making in literary translation and discusses implications for translator training and human-AI collaboration.}, url = {https://aclanthology.org/2026.nettit-1.18} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{alhuthali-zou-orasan:2026:NeTTIT, author = {Al-Huthali, Najat B. and Zou, Yuan and Orasan, Constantin}, title = {Fine-tuning Arabic-English NMT Engines for Investment Laws}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {146--156}, abstract = {The growing volume of legislative and regulatory translation is Saudi Arabia has created an increasing need for reliable Arabic-English machine translation (MT) solutions in the legal domain. However, Neural Machine Translation (NMT) systems trained on general-domain data perform poorly on legal texts due to terminological density, formulaic structures, and jurisdiction-specific drafting conventions. A major obstacle to domain adaptation for Arabic legal MT is the scarcity of large, high-quality, and publicly accessible parallel corpora representing national legislation. This study addresses this gap by constructing a specialised Arabic–English parallel corpus of Saudi investment laws and regulations compiled from official governmental sources. The paper documents the challenges involved in legal text extraction, OCR processing, cleaning, and alignment. Using this corpus, a pilot domain-adaptation experiment is conducted by fine-tuning the OPUS-MT Arabic–English model. Preliminary evaluation indicates that exposure to in-domain legal data improves terminology handling and structural adequacy compared to the baseline generic model.}, url = {https://aclanthology.org/2026.nettit-1.19} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}:0000-0003-2067-8890 @InProceedings{molscases:2026:NeTTIT, author = {Molés Cases, Teresa}, title = {Semantic typology and audio description. A corpus-based study of motion events}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {9--16}, abstract = {Audio description for the blind and visually impaired has received little attention within the Thinking-for-speaking and the Thinking-for-translating hypotheses, which have been mainly tested through narrative texts and motion event lexicalization. This study examines how motion events (i.e., action scenes) are lexicalized in the English and Spanish audio descriptions of the Irish animated film Wolfwalkers. The contribution seeks to determine whether the Spanish audio description is the result of intersemiotic or interlinguistic transfer and it examines how the audio-describer's mother tongue, time constraints, and audio description guidelines jointly shape the film experience for the visually impaired.}, url = {https://aclanthology.org/2026.nettit-1.2} } Author{1}{Orcid}: @InProceedings{alsharou:2026:NeTTIT, author = {Al Sharou, Khetam}, title = {\#MTFails to \#MTWins: Investigating User Perspectives on Machine Translation in Social Media}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {157--166}, abstract = {This work investigates the experience of Arabic and English-speaking users with Machine Translation on social media, examining how users engage with, and perceive, automated translation tools. It explores the challenges users face as well as the benefits of overcoming language barriers in real-time communication, as reflected in their social media posts. The study also examines how Machine Translation affects social media engagement, access to information, and the overall user experience, providing insights into how these tools shape online interactions and influence user satisfaction. The study highlights that users continue to have doubts about the effectiveness of Machine Translation in facilitating multilingual and online communication. It emphasises the importance of addressing users' perceptions and misconceptions about Machine Translation, as these can significantly affect the acceptance and adoption of emerging technologies, such as generative AI.}, url = {https://aclanthology.org/2026.nettit-1.20} } Author{1}{Orcid}: @InProceedings{calvodelbarrio-snchezgijn:2026:NeTTIT, author = {Calvo del Barrio, Sofía and Sánchez Gijón, Pilar}, title = {Comparative analysis of the double-stage revision phase (English-Italian) with an AI chatbot assistance}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {167--188}, abstract = {This paper draws on the contribution under the role of a project manager (PM) in a collaborative project between the Directorate-General for Translation (DGT) and the European Master's in Translation (EMT) to localise the MMLU dataset into eleven European languages. Among the translation teams in which the project manager role is present (English-Portuguese, English-English-Greek, and English-Spanish), the English-to-Italian team is the largest, with 103 participants, and has produced the most substantial contributions, a total of 943,000 words. The particular feature of this linguistic pair in the present project lies in the fact that the workflow comprises a translation stage followed by a double revision phase, whereas the workflow of the rest of linguistic pairs involved in the project consists of a translation and a revision phase. The aim of the present study is to assess the characteristics of the readjustments made in each of the two revision phases, with a GenAI chatbot as an assistant tool. The objective of the analysis is to assess whether the tool accurately identifies and classifies readjustments across the two revision stages, thereby enabling the PM to save time in this process and to focus on using the information provided to design and improve workflows.}, url = {https://aclanthology.org/2026.nettit-1.21} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{li-weng:2026:NeTTIT1, author = {Li, Jingyi and Weng, Yu}, title = {Does AI-INT Training Transfer Across Interpreting Modes? Evidence from Eye-Tracking}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {189--192}, abstract = {This paper investigates two related questions: whether current AI-assisted interpreting (AI-INT) training helps students process AI-generated transcripts more efficiently, and whether such training also benefits conventional text-based interpreting tasks. To answer these questions, forty-one postgraduate interpreting students (22 trained, 19 untrained) completed three types of tasks: AI-assisted simultaneous interpreting (AISI), simultaneous interpreting with text (SIT), and sight translation (ST), yielding 454 valid eye-tracking trials. Fixation count and mean fixation duration (MFD) were used to gauge reading ease and efficiency. Trained interpreters produced significantly fewer fixations than untrained interpreters in SIT (d = 0.38, p = .04) and ST (d = 0.35, p = .03) conditions, but no comparable advantage emerged in the AISI mode (p = .69). MFD did not differ significantly between groups in any mode. These findings suggest that current AI-INT training may enhance reading efficiency more clearly in static text conditions than in the dynamic transcript environment of AI-assisted interpreting, highlighting the need for more targeted instruction in transcript-monitoring skills.}, url = {https://aclanthology.org/2026.nettit-1.22} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{zta-alvarezvidal-olivergonzalez:2026:NeTTIT, author = {Öztaş, Seyda and Alvarez Vidal, Sergi and Oliver Gonzalez, Antoni}, title = {Using LLMs for Glossary-Constrained Terminology Correction in Machine Translation}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {193--197}, abstract = {This paper analyzes the use of large language models (LLMs) for terminology correction in machine translation (MT), a critical requirement in domain-specific and industrial settings. While modern MT systems often produce fluent outputs, they frequently fail to enforce consistent use of predefined terminology. We use LLM-based correction as a controlled setting to study how terminology errors are handled in practice. We introduce a terminology-level evaluation framework that distinguishes between true substitutions and partial corrections, where the correct term is introduced without removing the incorrect one. Our results show that standard MT metrics and term-presence heuristics are insufficient for evaluating terminology-sensitive tasks. Additionally, referenceless COMET-QE analysis reveals a trade-off between terminology accuracy and overall translation quality. These findings highlight the need for improved evaluation and control mechanisms to ensure reliable terminology correction in MT systems.}, url = {https://aclanthology.org/2026.nettit-1.23} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}: @InProceedings{li-weng:2026:NeTTIT2, author = {Li, Yanze and Weng, Yu}, title = {Flow as Regulated Engagement in Chinese–English Translation: Evidence from Self-Report, Eye Tracking, and Physiological Measures}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {198--202}, abstract = {This paper examines how flow experience, as a task-related psychological state, relates to effort investment, attention allocation, monitoring behavior, and physiological regulation during Chinese–English translation. It synthesizes evidence from three studies within the same research project: a task-based self-report study and two laboratory analyses of the same experimental dataset, focusing respectively on gaze behavior and physiological responses. The self-report findings showed that flow was primarily marked by challenge–skill balance, a sense of control, and reduced self-consciousness. The laboratory findings further indicated that, under positive affective priming, higher flow was associated with smoother ST–TT coordination, lower cognitive effort, and a more regulated physiological profile. However, regression counts did not indicate a simple reduction in monitoring behavior. Taken together, the findings characterize flow in translation as a form of regulated engagement, in which effort and attention are more efficiently organized while the control demands of translation remain present. This account extends the study of flow beyond self-reported experience by incorporating process-level evidence and provides a basis for considering translators' cognitive engagement and sustainable working modes in current technology-mediated translation contexts.}, url = {https://aclanthology.org/2026.nettit-1.24} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{mirdjonovna-EtAl:2026:NeTTIT, author = {Mirdjonovna, Shahlo Khamroyeva and Nuriddinova, Gulirano Baxtiyor qizi and Kadirovna, Mehriniso Kilicheva and Boltaeva, Gulrukh Erkin kizi and Qurbonova, Nodira Roziqovna and Kamalova, Aziza Islomovna}, title = {Development of an Uzbek–English Parallel Corpus Search System: A Case Study of Literary Works}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {203--206}, abstract = {This paper presents the development of an Uzbek–English parallel corpus search system based on literary texts. The aim of the study is to facilitate bilingual text analysis and improve access to aligned linguistic data for translation and research purposes. A corpus of more than 20,000 sentence pairs was compiled from seven literary works and processed using alignment techniques. The developed system enables users to perform concordance searches and retrieve parallel sentence pairs efficiently. It also provides extra-linguistic information, such as the source of each sentence, which enhances usability. The interface is designed to be simple and user-friendly for both researchers and students. The results show that the system can effectively support translation studies, corpus linguistics, and natural language processing applications. This work contributes to the development of digital resources for the Uzbek language and highlights the importance of parallel corpora for low-resource languages.}, url = {https://aclanthology.org/2026.nettit-1.25} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}: Author{4}{Orcid}: Author{5}{Orcid}: Author{6}{Orcid}: @InProceedings{staiano-EtAl:2026:NeTTIT, author = {Staiano, Maria Carmen and Monti, Johanna and Chiusaroli, Francesca and Tykhonov, Slava and Hawkins, Ken and Jacot des Combes, Hélène and Yang, Saini and Fra Paleo, Urbano and Murray, Virginia}, title = {Benchmarking Multilingual Terminology Translation for UNDRR-ISC Hazard Information Profiles}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {207--230}, abstract = {This paper presents the results of the multilingual translation of the 281 UNDRR-ISC Hazard Information Profiles (HIPs) terms from English into French, Spanish, and Chinese, in response to a request from UN partners, member states, and the scientific community. We compare three translation setups: (1) gpt-oss-20b as an open-weight single-LLM baseline; (2) ChatGPT-5.5 as a state-of-the-art proprietary baseline; and (3) a Voter-Arbitrator multi-agent architecture, which combines three models through consensus-based arbitration, served locally via Ollama. Outputs are aligned with a SKOS-based hazard knowledge organisation system, enabling machine-actionable multilingual terminology interoperable with semantic disaster risk infrastructures. Evaluation against a human-validated gold standard shows that ChatGPT-5.5 achieves the highest Exact Match accuracy across all languages, while the Voter-Arbitrator system consistently outperforms the open-weight baseline on Exact Match (58.0\% vs 45.6\% for French) and achieves competitive cosine similarity, though human evaluation reveals a higher rate of conceptually incorrect terms for Chinese. The findings suggest that consensus-based multi-agent arbitration offers a reproducible, sovereign, and infrastructure-independent alternative to proprietary systems, with performance gains over single open-weight model inference.}, url = {https://aclanthology.org/2026.nettit-1.26} } Author{1}{Orcid}:https://orcid.org/0000-0003-1458-5225 Author{2}{Orcid}: Author{3}{Orcid}: Author{4}{Orcid}: Author{5}{Orcid}: Author{6}{Orcid}: Author{7}{Orcid}: Author{8}{Orcid}: Author{9}{Orcid}: @InProceedings{raka:2026:NeTTIT, author = {Źrałka, Edyta}, title = {Translation Quality Assessment of legal translations by MateCat and its use in future translation didactics}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {231--237}, abstract = {The paper introduces the principles of a research undertaken to enrich MateCat's database with the legal terminology of English, Polish and Romance languages (French, Italian, Spanish and Portuguese) and, based on it, to perform specialised legal translations with post-editing (PE). The introductory stage of the research led to the development of criteria for a manual metric necessary to carry out the Translation Quality Assessment (TQA) and PE processes rather than performing traditional translation without the involvement of electronic tools. The research data will be used to develop a teaching programme for educating students in TQA and PE, and, possibly, also to produce a computer programme to support it.}, url = {https://aclanthology.org/2026.nettit-1.27} } Author{1}{Orcid}: @InProceedings{liyanapathirana-EtAl:2026:NeTTIT, author = {Liyanapathirana, Jeevanthi Uthpala and Bouillon, Pierrette and Mutal, Jonathan David and Girletti, Sabrina and Volkart, Lise}, title = {COPECO-Speech: Integrating Speech and LLMs for Post-Editing in Translation Workflows}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {238--244}, abstract = {In this paper, we explore how large language models (LLMs) can be integrated into translation workflows through multimodal interaction. Recent developments in generative Artificial Intelligence (AI) are influencing the role of translators, yet LLMs are often used in automatic ways allowing limited user control over editing. We present COPECO-Speech, a multimodal platform for translation and post-editing. It allows users to interact with the translation process through typing, speech based navigation commands and spoken natural-language instructions interpreted by an LLM. The system is web-based and combines automatic speech recognition (ASR), machine translation (MT), and LLM-based editing within a single workflow, allowing more interactive and flexible translation and post-editing. COPECO-Speech is a work in progress, currently relying on a backend prompt to guide translation outputs based on user instructions, with future work exploring contextual prompting and its impact on translation quality.}, url = {https://aclanthology.org/2026.nettit-1.28} } Author{1}{Orcid}:https://orcid.org/0009-0002-1020-1300 Author{2}{Orcid}: Author{3}{Orcid}: Author{4}{Orcid}: Author{5}{Orcid}: @InProceedings{picazoizquierdo-lamsiyah-mitkov:2026:NeTTIT, author = {Picazo-Izquierdo, Alicia and Lamsiyah, Salima and Mitkov, Ruslan}, title = {Translation Memory Systems: Evolution, Retrieval Methods, and Human-Centred Perspectives}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {245--253}, abstract = {This paper traces the evolution of translation memory systems (TMS) from early lexical and edit-distance matching to contemporary semantic and neural retrieval approaches. It reviews the historical development of TMS, their role within computer-aided translation (CAT) workflows, and their function as both technical infrastructures and human-computer interaction (HCI) systems. The paper examines the structure of translation memories, common match types, and the cognitive and usability factors that shape translators' interaction with these tools. It also considers the broader social and professional ecosystem of TMS, including public resources, workshops, and user adoption. Finally, it discusses the limits of current retrieval methods, the importance of translation memory cleaning, and future challenges involving explainability, hybrid retrieval, and AI-assisted translation workflows.}, url = {https://aclanthology.org/2026.nettit-1.29} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}: @InProceedings{zhang-hao-li:2026:NeTTIT, author = {Zhang, Shuyin and Hao, Xindi and Li, Zhenhai}, title = {How Well Can Large Language Models Detect, Classify, and Correct Errors in Chinese-English Public Sign Translations?}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {17--28}, abstract = {This study evaluates six frontier large language models on their ability to detect, classify, and correct errors in Chinese–English public sign translations. Model performance is assessed through both automatic and human evaluation, using a 100-item test set constructed from authentic bilingual signage in China. Results show that all models perform strongly in error detection but vary in classification and correction, with different models excelling at different error types. This suggests that a multi-model workflow may be more effective than relying on any single model.}, url = {https://aclanthology.org/2026.nettit-1.3} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}: @InProceedings{tadi-stojak:2026:NeTTIT, author = {Tadić, Marko and Stojak, Mladen}, title = {Project HRVOJE-M: integration of Croatian LLM with a TBMS}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {254--259}, abstract = {The paper describes the project HRVOJE-M that was selected for funding under EU programme Competitiveness and Cohesion within the IRI S3 call. The first goal of the project is to produce a LLM trained exclusively on Croatian data of at least 20 billion tokens in size. The second goal is the integration of that LLM in the proprietary TBMS Orchestrum and its offer to different users through API.}, url = {https://aclanthology.org/2026.nettit-1.30} } Author{1}{Orcid}:https://orcid.org/0000-0001-6325-820X Author{2}{Orcid}: @InProceedings{yujie-xu:2026:NeTTIT, author = {Yujie, Huang and Xu, Han}, title = {Training Interpreting Learners to Collaborate with Captions and Subtitles during Simultaneous Interpreting}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {29--33}, abstract = {To help interpreting learners make better use of these tools during simultaneous interpreting, the present study developed a specialised training model based on learners' needs and investigated its impact on learners' interaction with captions and subtitles using eye-tracking technology. *The study reported in this paper forms part of the first author's ongoing PhD research, which is supervised by the second author author info: Yujie Huang https://orcid.org/0009-0005-9783-4830 Yujie Huang is a PhD student at The Hong Kong Polytechnic University. She is particularly interested in applying interdisciplinary approaches to the investigation of interpreting and translation activities. By collecting and analyzing empirical evidence, she seeks to address practical challenges in interpreter training, evaluation and assessment, interpreting cognition, and the integration of technology in interpreting. Han Xu https://orcid.org/0000-0001-5493-6857 Han Xu is interested in conducting interdisciplinary studies to empirically investigate different aspects of interpreting and translation activity, such as issues related to quality, ethics, training and professionalism. Her research works are published in scholarly journals in the fields, such as Across Languages and Cultures, Lingua, Meta, Multilingua, Perspectives, Translation \& Interpreting, Translation and Interpreting Studies, and Chinese Translators Journal.}, url = {https://aclanthology.org/2026.nettit-1.4} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{shi-he:2026:NeTTIT, author = {SHI, Jiawei and HE, Sihui}, title = {Punctuation Patterns in Translated and Original Chinese-English Literary Texts: A Weibull Distribution Analysis}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {34--40}, abstract = {Recent research reveals that punctuation patterns exhibit cross-linguistic universal features, as the intervals between consecutive punctuation marks follow a discrete Weibull distribution. While this mathematical regularity provides a valuable tool for quantitative linguistics, its application to translated texts remains underexplored. Translation alters punctuation through the tension between preserving source text style and adhering to target language structural rules. To investigate this, we constructed a bidirectional corpus of twenty-eight classic twentieth-century novels and their authoritative translations, comprising Original Chinese (OC), Original English (OE), English-to-Chinese (TC), and Chinese-to-English (TE). We modeled punctuation intervals across these subcorpora using the discrete Weibull distribution. Results confirm a good fit across all subcorpora, reinforcing cross-linguistic universality. Parameter analysis reveals distinct signatures. Chinese texts exhibit lower punctuation frequency and higher rhythmic regularity, whereas English texts show the inverse. Crucially, translation effects are directionally asymmetric. English-to-Chinese translations exceed target language baselines, while Chinese-to-English texts settle at intermediate values between the source and target. Despite this asymmetry, both directions converge toward target language conventions, supporting the normalization hypothesis. Alongside these findings, the lightweight and interpretable parameters can serve as metrics for automatic translationese detection, bridging quantitative linguistics and practical translation technology.}, url = {https://aclanthology.org/2026.nettit-1.5} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{dumaniacar:2026:NeTTIT, author = {Dumančić Acar, Ana}, title = {From SOP to AI-Native Pipeline: Design and Evaluation of a Translation-Memory-Grounded LLM Workflow (Nixa AI)}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {41--45}, abstract = {This paper presents Nixa AI, an internally developed AI-first translation system built within a professional Language Service Provider environment. Unlike approaches that layer Large Language Models onto existing CAT tools, Nixa AI is designed as a fully AI-native pipeline grounded in established workflows and translation memory practices. The system combines a translation memory cascade with glossary enforcement and multi-stage LLM processing. Previously approved translations are prioritized, while LLMs operate in a constrained setting to ensure consistency and format preservation. Automated quality assurance and targeted repair mechanisms are integrated to detect and correct errors before human review. Evaluation on real client projects shows strong performance, particularly in structured and terminology-rich content. However, recurring issues include fidelity errors, terminology inconsistencies, and formatting deviations, often masked by high fluency. The key finding is that the main limitation is not technical capability but trust. In professional contexts, human validation remains essential, shifting the translator's role from production to final accountability.}, url = {https://aclanthology.org/2026.nettit-1.6} } Author{1}{Orcid}: @InProceedings{snchezgijn-EtAl:2026:NeTTIT, author = {Sánchez-Gijón, Pilar and Valdez, Susana and Calvo Del Barrio, Sofía and Bellemont, Florence and Kokkinidou, Anna and Brasoveanu, Mihai Cristian}, title = {Building a European Multilingual Evaluation Dataset: The MMLU Localisation Project within the EMT Network}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {46--53}, abstract = {This paper reports on a collaboration between the Directorate-General for Translation (DGT) and the European Master's in Translation (EMT) to localise the MMLU dataset into 11 European languages. Beyond creating a more inclusive benchmark for LLM evaluation, the project offers master's students authentic, project-based professional training in translation, revision, project management, and multilingual coordination, while highlighting key methodological, administrative, and workflow challenges.}, url = {https://aclanthology.org/2026.nettit-1.7} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}: Author{4}{Orcid}: Author{5}{Orcid}: Author{6}{Orcid}: @InProceedings{rebechi-alves:2026:NeTTIT, author = {Rebechi, Rozane and Alves, Diego}, title = {A Comparative Study of LLMs for Idiomatic Expression Identification and Translation Equivalent Extraction in an English–Portuguese Subtitle Corpus}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {54--62}, abstract = {natural language processing due to their noncompositionality and dependence on conventionalized usage. This study investigates the ability of large language models (LLMs) to identify IEs in English and to locate their corresponding translations in Brazilian Portuguese within a parallel subtitle corpus. Using a subset of 10,000 aligned sentence pairs from the OpenSubtitles corpus, two multilingual LLMs were evaluated through zero-shot prompting in two tasks: (i) idiom detection in English sentences and (ii) extraction of their translated equivalents in Portuguese. Model outputs were manually annotated by an expert linguist to assess accuracy and to classify translation strategies based on degrees of idiomaticity and conventionality. Results reveal substantial variation in how models operationalize idiomaticity, with openai/gpt-oss-20b achieving higher precision in idiom identification, while metallama/ Llama-3.3-70B-Instruct adopts a broader and more permissive interpretation. In the translation identification task, both models demonstrate strong performance in identifying corresponding translations, with most choices classified as either idiomatic and conventional or non-idiomatic but acceptable. Qualitative analysis further shows that LLMs rely on contextual and intertextual cues, sometimes inferring idiomatic meanings beyond the surface form, but also producing false positives driven by lexical familiarity or stylistic features.}, url = {https://aclanthology.org/2026.nettit-1.8} } Author{1}{Orcid}: Author{2}{Orcid}: @InProceedings{lee-ryu-yu:2026:NeTTIT, author = {Lee, Jinam and Ryu, Jaehee and Yu, Janet Sohlhee}, title = {Navigating the "Fluency Trap": Cognitive Risks and Structural Challenges in Korean-English Patent Translation Post-Editing}, booktitle = {Proceedings of the Conference on New Trends in Translation and Interpreting Technology 2026}, month = {June}, year = {2026}, address = {Dubrovnik, Croatia}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, pages = {63--67}, abstract = {This study investigates the fluency trap in Korean (KR) – English (EN) patent translation, where advanced NMT/LLM outputs mask critical technical errors through deceptive syntactic coherence. We analyze how KR-specific structural disparities, such as zero-anaphora and head-final dependencies, lead to silent omissions that fatally distort a claim's legal scope. Our findings reveal a cognitive halo effect, where linguistic smoothness erodes the analytical scrutiny required to detect misattributed technical agency. To mitigate this risk, we propose the Critical Revision Model (CRM), shifting the post-editing paradigm from stylistic refinement to adversarial technical verification. We conclude that the legal integrity of intellectual property depends not on linguistic naturalness, but on the rigor of human-led syntactic and technical auditing.}, url = {https://aclanthology.org/2026.nettit-1.9} } Author{1}{Orcid}: Author{2}{Orcid}: Author{3}{Orcid}: