  With the advancement in argument detection, we suggest to pay more attention
to the challenging task of identifying the more convincing arguments. Machines
capable of responding and interacting with humans in helpful ways have become
ubiquitous. We now expect them to discuss with us the more delicate questions
in our world, and they should do so armed with effective arguments. But what
makes an argument more persuasive? What will convince you? In this paper, we
present a new data set, IBM-EviConv, of pairs of evidence labeled for
convincingness, designed to be more challenging than existing alternatives. We
also propose a Siamese neural network architecture shown to outperform several
baselines on both a prior convincingness data set and our own. Finally, we
provide insights into our experimental results and the various kinds of
argumentative value our method is capable of detecting.
