This paper introduces GENTRAC, an open-access web-based tool built to interactively detect and analyze potentially traumatic content in witness statements of genocide and mass atrocity trials. Harnessing recent developments in natural language processing (NLP) to detect trauma, GENTRAC processes and formats court transcripts for NLP analysis through a sophisticated parsing algorithm and detects the likelihood of traumatic content for each speaker segment. The tool visualizes the density of such content throughout a trial day and provides statistics on the overall amount of traumatic content and speaker distribution. Capable of processing transcripts from four prominent international criminal courts, including the International Criminal Court (ICC), GENTRAC’s reach is vast, tailored to handle millions of pages of documents from past and future trials. Detecting potentially re-traumatizing examination methods can enhance the development of trauma-informed legal procedures. GENTRAC also serves as a reliable resource for legal, human rights, and other professionals, aiding their comprehension of mass atrocities’ emotional toll on survivors.

Recent progress in natural language processing has been impressive in many different areas with transformer-based approaches setting new benchmarks for a wide range of applications. This development has also lowered the barriers for people outside the NLP community to tap into the tools and resources applied to a variety of domain-specific applications. The bottleneck however still remains the lack of annotated gold-standard collections as soon as one’s research or professional interest falls outside the scope of what is readily available. One such area is genocide-related research (also including the work of experts who have a professional interest in accessing, exploring and searching large-scale document collections on the topic, such as lawyers). We present GTC (Genocide Transcript Corpus), the first annotated corpus of genocide-related court transcripts which serves three purposes: (1) to provide a first reference corpus for the community, (2) to establish benchmark performances (using state-of-the-art transformer-based approaches) for the new classification task of paragraph identification of violence-related witness statements, (3) to explore first steps towards transfer learning within the domain. We consider our contribution to be addressing in particular this year’s hot topic on Language Technology for All.