DuaPIN: Auxiliary task enhanced dual path interaction network for civil court view generation

Civil court view generation (CCVG) is a novel but important task for legal intelligence that aims to automatically generate a judge’s opinion based on the plaintiff’s claims and fact descriptions to interpret the judgment result. The task is more challenging than criminal court view generation as the latter generates views based only on criminal facts as input, whereas the CCVG must consider both the plaintiff’s claims and civil facts under the principle of “no claim, no trial.” However, current approaches still follow criminal domain practices to solve problems in civil cases. Moreover, the explicit modeling of the potential correspondence between claims and facts has often been neglected, as court views are required to respond to each corresponding claim based on factual evidence. To address the issues, we propose a dual path interaction network augmented by two self-supervised auxiliary tasks (named DuaPIN), which follows a bionic design by simulating the thinking logic of judges when writing opinions. Specifically, we construct a structurally symmetric Transformer-based dual path multi-encoder–decoder model such that the two inputs, claim and fact, contribute equally to the generation of civil court views. Moreover, an auxiliary task enhanced (ATE) training paradigm using multiple DuaPIN decoders is proposed to explicitly model the potential correspondence between claims and facts. Extensive experiments on public legal document dataset demonstrated that DuaPIN achieves competitive performance compared with previous methods and offers certain performance improvements to popular pre-trained language models via the ATE training method.