IARPA’s Better Extraction from Text Towards Enhanced Retrieval (BETTER) Program created multiple multilingual datasets to spawn and evaluate cross-language information extraction and information retrieval research and development in zero-shot conditions. The first set of these resources for information extraction, the “Abstract” data will be released to the public at LREC 2022 in four languages to champion further information extraction work in this area. This paper presents the event and argument annotation in the Abstract Evaluation phase of BETTER, as well as the data collection, preparation, partitioning and mark-up of the datasets.

This paper will detail how IARPA’s MATERIAL Cross-Language Information Retrieval (CLIR) program investigated certain linguistic parameters to guide language choice, data collection and partitioning, and understand evaluation results. Discerning which linguistic parameters correlated with overall performance enabled the evaluation of progress when different languages were measured, and also was an important factor in determining the most effective CLIR pipeline design, customized to handle language-specific properties deemed necessary to address.