This paper presents an overview of the SIGTURK 2026 Shared Task on Terminology-Aware Machine Translation for English-Turkish Scientific Texts. We address the critical challenge of terminological accuracy in low-resource settings by constructing the first terminology-rich English-Turkish parallel corpus, comprising 3,300 sentence pairs from STEM domains with 10,157 expert-validated term pairs. The shared task consists of three subtasks: term detection, expert-guided correction, and end-to-end post-editing. We evaluate state-of-the-art baselines (including GPT-5.2 and Claude Sonnet 4.5) alongside participant systems employing diverse strategies from fine-tuning to Retrieval-Augmented Generation (RAG). Our results highlight that while massive generalist models dominate zero-shot detection, smaller, domain-adapted models using Supervised Fine-Tuning and Reinforcement Learning can significantly outperform them in end-to-end post-editing. Furthermore, we find that rigid retrieval pipelines often disrupt fluency, whereas Chain-of-Thought prompting allows models to integrate terminology more naturally. Despite these advances, a significant gap remains between automated systems and human expert performance in strict terminology correction.

Dialogue State Tracking (DST) is crucial for understanding user needs and executing appropriate system actions in task-oriented dialogues. Majority of existing DST methods are designed to work within predefined ontologies and assume the availability of gold domain labels, struggling with adapting to new slots values. While Large Language Models (LLMs)-based systems show promising zero-shot DST performance, they either require extensive computational resources or they underperform existing fully-trained systems, limiting their practicality. To address these limitations, we propose a zero-shot, open-vocabulary system that integrates domain classification and DST in a single pipeline. Our approach includes reformulating DST as a question-answering task for less capable models and employing self-refining prompts for more adaptable ones. Our system does not rely on fixed slot values defined in the ontology allowing the system to adapt dynamically. We compare our approach with existing SOTA, and show that it provides up to 20% better Joint Goal Accuracy (JGA) over previous methods on datasets like MultiWOZ 2.1, with up to 90% fewer requests to the LLM API.

Recently, there has been growing interest within the community regarding whether large language models are capable of planning or executing plans. However, most prior studies use LLMs to generate high-level plans for simplified scenarios lacking linguistic complexity and domain diversity, limiting analysis of their planning abilities. These setups constrain evaluation methods (e.g., predefined action space), architectural choices (e.g., only generative models), and overlook the linguistic nuances essential for realistic analysis. To tackle this, we present PARADISE, an abductive reasoning task using Q&A format on practical procedural text sourced from wikiHow. It involves tip and warning inference tasks directly associated with goals, excluding intermediary steps, with the aim of testing the ability of the models to infer implicit knowledge of the plan solely from the given goal. Our experiments, utilizing fine-tuned language models and zero-shot prompting, reveal the effectiveness of task-specific small models over large language models in most scenarios. Despite advancements, all models fall short of human performance. Notably, our analysis uncovers intriguing insights, such as variations in model behavior with dropped keywords, struggles of BERT-family and GPT-4 with physical and abstract goals, and the proposed tasks offering valuable prior knowledge for other unseen procedural tasks. The PARADISE dataset and associated resources are publicly available for further research exploration with https://anonymous.4open.science/r/paradise-53BD/README.md.