text: 107,66,1383090332401799171,2485053080,Swarnadeep Saha,"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 🙂 Joint work w. @prateeky2806 @lbauer119 @mohitban47 @uncnlp Paper: <LINK> 1/5 <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 & 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; & 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).",https://arxiv.org/abs/2104.07644,"Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context. Discriminative tasks are limiting because they fail to adequately evaluate the model's ability to reason and explain predictions with underlying commonsense knowledge. They also allow such models to use reasoning shortcuts and not be ""right for the right reasons"". In this work, we present ExplaGraphs, a new generative and structured commonsense-reasoning task (and an associated dataset) of explanation graph generation for stance prediction. Specifically, given a belief and an argument, a model has to predict if the argument supports or counters the belief and also generate a commonsense-augmented graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. We collect explanation graphs through a novel Create-Verify-And-Refine graph collection framework that improves the graph quality (up to 90%) via multiple rounds of verification and refinement. A significant 79% of our graphs contain external commonsense nodes with diverse structures and reasoning depths. Next, we propose a multi-level evaluation framework, consisting of automatic metrics and human evaluation, that check for the structural and semantic correctness of the generated graphs and their degree of match with ground-truth graphs. Finally, we present several structured, commonsense-augmented, and text generation models as strong starting points for this explanation graph generation task, and observe that there is a large gap with human performance, thereby encouraging future work for this new challenging task. ExplaGraphs will be publicly available at this https URL ","ExplaGraphs: An Explanation Graph Generation Task for Structured