We present a new NLP task and dataset from the domain of the U.S. civil procedure. Each instance of the dataset consists of a general introduction to the case, a particular question, and a possible solution argument, accompanied by a detailed analysis of why the argument applies in that case. Since the dataset is based on a book aimed at law students, we believe that it represents a truly complex task for benchmarking modern legal language models. Our baseline evaluation shows that fine-tuning a legal transformer provides some advantage over random baseline models, but our analysis reveals that the actual ability to infer legal arguments remains a challenging open research question.

In an ideal world, the Supreme Court would provide stability in the run-up to a presidential election, imposing uniform rules based on long-accepted principles of election law. We do not live in that world. One week out from the 2020 election, four Supreme Court justices have launched a scorched-earth mission against voting rights. They teed up a Bush v. Gore reprise that could hand Donald Trump an unearned victory. These justices are in open revolt against voting rights, abandoning the pretense of "voter fraud" and embracing state legislatures' right to disenfranchise their constituents.