Their goal is to check if the attention heads of a given pre-trained model can track syntactic relations better than chance or baselines.
raganato and Tiedemann (2018) analyze a machine translation model's encoder by extracting dependency trees from its self-attention weights, using Chu-Liu/Edmonds algorithm.