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We propose a new shared task of semantic retrieval from legal texts, in whicha so-called contract discovery is to be performed, where legal clauses areextracted from documents, given a few examples of similar clauses from otherlegal acts. The task differs substantially from conventional NLI and sharedtasks on legal information extraction (e.g., one has to identify text spaninstead of a single document, page, or paragraph). The specification of theproposed task is followed by an evaluation of multiple solutions within theunified framework proposed for this branch of methods. It is shown thatstate-of-the-art pretrained encoders fail to provide satisfactory results onthe task proposed. In contrast, Language Model-based solutions perform better,especially when unsupervised fine-tuning is applied. Besides the ablationstudies, we addressed questions regarding detection accuracy for relevant textfragments depending on the number of examples available. In addition to thedataset and reference results, LMs specialized in the legal domain were madepublicly available.

Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines