This shared task focuses on advancing computational approaches to understanding Akkadian, a Semitic language of the ancient Near East. Participants are invited to tackle two challenging tasks designed to test their systems’ ability to process and interpret Akkadian texts, which often exhibit unique linguistic complexities.

Lemma Prediction in Context

The first task is to predict the correct lemma for each word in a given sentence. Unlike straightforward lemmatization, this task requires systems to account for contextual nuances that influence lemma assignment. Akkadian, with its extensive use of homographs and context-dependent morphology, makes this problem particularly intricate. For instance, the same surface form may correspond to multiple lemmas depending on its syntactic or semantic environment.

Text Restoration

The second task involves completing sentences with missing words. Participants will receive texts where certain words are replaced by placeholders, and their goal is to predict the correct form to fill these gaps. This task evaluates systems’ ability to understand broader sentence structure, context, and linguistic conventions of Akkadian. Success in this task demonstrates a system’s proficiency in both lexical and syntactic reconstruction.