PREDICTIVE MODEL FOR RANKING ARGUMENT CONVINCINGNESS OF TEXT PASSAGES

Aspects of the present disclosure relate to systems and methods for identifying and providing a passage that is highly convincing in terms of one or more stances for a given topic. A convincingness ranking model is based on a machine learning model, a multi-layer feed forward neural network with a back propagation for updating for example. The query represents a stance under a topic. A convincingness ranking model trainer identifies passage pairs that relate to the query, and labels the passage pairs according to relative levels of convincingness from the stance between the two passages in the respective passage pairs. The trainer provides a user interface to interactively receive selections of the relative levels of convincingness of the passage pairs. The system ranks passages from the passage pairs based on the labels and filter out specific passages when the rank forms a cyclic relationship in a directed graph. The convincingness ranking model trainer uses the remaining message pair and the labels to train the convincingness ranking model.