BLM-CausT

Description

BLM-CausT is a dataset in Turkish for learning the causative alternation developed in the Blackbird Language Matrices (BLM) framework. In this task, an instance consists of sequences of sentences with specific attributes. To predict the correct answer as the next element of the sequence, a model must correctly detect the underlying generative rules used to produce the dataset.

The data for Turkish are collected from news and non-fiction sources (Penn v. 2.163; 183,555 tokens, 16,396 trees) and grammar and dictionary examples (Kenet v. 2.164; 178,658 tokens, 18,687 trees). The query collects sentences where the main verb is annotated with the VOICE parameter. 

The data comes grouped by target voice, in two groups SENT (full sentences) and VERB (verb only) and each subset is split into train/test. The statistics of the current iteration of the dataset are (train:test split information):

Reference

If you use this dataset, please cite the following publication:

Giuseppe Samo, Paola Merlo, Modelling the Morphology of Verbal Paradigms: A Case Study in the Tokenization of Turkish and Hebrew, paper accepted at the SigTurk – SIGTURK 2026 Workshop