Organizing Contexts as a Lattice of Decision Trees for Machine Reading Comprehension
Supported decision trees that have been first proposed to boost the performance and the explainability of the expert systems built upon the texts can become a great basis for the machine reading comprehension (MRC) systems. The supported decision tree is based on building and combining the corresponding discourse trees for the text passage. In this work, we build an environment of supported decision trees for the MRC task. Each answer is represented by a path of a supported decision tree and the whole corpus of answers is then form a lattice of supported decision trees. This environment gives a boost to MRC performance, handling cases where it is nontrivial to determine which document/passage MRC needs to be applied to.