Question Answering is an important and broad field of research within Natural Language Processing. Previous editions of PolEval included passage retrieval tasks, which are crucial in narrowing down the range of documents relevant to a human question, but only after including a reading comprehension system, can the whole process of answering a question be fully automated. [Score]

Understanding human emotions is one of the more challenging tasks in natural language processing. Not only are they a very subjective topic, but humans also often lack the capability to fully express themselves in written language. Understanding the expressed emotions can require some additional context, sometimes given by external knowledge. [PolevalFinalF1]

Automatic speech recognition (ASR) has made significant progress over the last decade. Improvements in deep learning and increased data availability have resulted in accuracy levels for artificial speech transcription that are on par with human transcription, at least in specific domains, tasks, and speech characteristics. ASR technology has expanded to cover many new languages, use cases, user demographics, and devices. However, achieving robust speech recognition remains a challenge for many low-resource languages, specific speaker groups, application domains, and acoustic conditions. [WER]