text:   Commonsense Reasoning",6,"['Excited to share our new work on ""ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning""! Has been a long effort and a great learning experience too 🙂\n\nJoint work w. @prateeky2806 @lbauer119 @mohitban47 @uncnlp\nPaper: <LINK>\n1/5 <LINK>', 'Commonsense reasoning tasks are usually discriminative, thus failing to eval. model\'s ability to reason+explain preds with underlying knowledge(+allowing shortcuts). We propose a new ""generative+structured"" task for generating ""commonsense expl. graphs"" for stance prediction. 2/5', 'Graphs are structured, so explicitly explain+eval model reasoning by visually laying out relevant context &amp; commonsense knowldg edges/chains/subgraphs. We collect a dataset of non-trivial, complete, unambiguous explanations thru Collect-Judge-Refine graph-annotation framework 3/5', ""We also propose a multi-level evaluation framework that checks for generated graph's structural + semantic correctness (as judged by stance inference capability given the belief and graph) + predicted graph’s plausibility wrt gold graphs + each individual edge's importance. 4/5"", 'Initial baselines w. BART+T5 show that they fail to generate meaningful expln graphs, leaving large gap with human performance; &amp; we hope this will encourage future work by community on better structured models for challenging new commonsense graph-based expln generation task 5/5', ""@aman_madaan @prateeky2806 @lbauer119 @mohitban47 @uncnlp Thanks, @aman_madaan for the appreciation and the pointer (we'll cite it in our next version).""]",21,04,1542