Timely and accurate situation awareness underpins effective decision-making in humanitarian response, conflict monitoring, and peacebuilding. Yet, synthesizing heterogeneous and rapidly evolving information from news, conflict databases, and economic indicators remains labor-intensive and delays critical interventions. We present a Retrieval-Augmented Generation (RAG) system that autonomously produces structured, evidence-backed situation awareness reports by integrating real-time data from GDELT, ACLED, ReliefWeb, and World Bank APIs. To rigorously assess report quality without ground-truth references, we introduce a three-level reference-free evaluation framework combining automated NLP metrics, expert review by United Nations crisis analysts, and scalable LLM-as-a-Judge assessment. In a multi-country study across 15 conflict-prone regions, our system generated coherent, relevant, and actionable reports, with UNDP analysts estimating a ~50% reduction in report preparation time. These findings demonstrate the feasibility of deploying RAG-based systems for peacebuilding and humanitarian operations and provide a reproducible framework for generating and evaluating AI-assisted situational intelligence at scale.

This paper presents the UNT HiLT+Ling system for the Sigmorphon 2019 shared Task 2: Morphological Analysis and Lemmatization in Context. Our core approach focuses on the morphological tagging task; part-of-speech tagging and lemmatization are treated as secondary tasks. Given the highly multilingual nature of the task, we propose an approach which makes minimal use of the supplied training data, in order to be extensible to languages without labeled training data for the morphological inflection task. Specifically, we use a parallel Bible corpus to align contextual embeddings at the verse level. The aligned verses are used to build cross-language translation matrices, which in turn are used to map between embedding spaces for the various languages. Finally, we use sets of inflected forms, primarily from a high-resource language, to induce vector representations for individual UniMorph tags. Morphological analysis is performed by matching vector representations to embeddings for individual tokens. While our system results are dramatically below the average system submitted for the shared task evaluation campaign, our method is (we suspect) unique in its minimal reliance on labeled training data.