Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents
This addresses a bottleneck in LegalAI for Chinese legal systems by enabling better handling of long documents, but it is incremental as it adapts an existing method to a specific domain.
The paper tackles the challenge of processing long legal documents in Chinese, which exceed the length limits of mainstream pre-trained language models, by introducing Lawformer, a Longformer-based model. It achieves promising improvements on tasks like judgment prediction and legal question answering, though specific numbers are not provided.
Legal artificial intelligence (LegalAI) aims to benefit legal systems with the technology of artificial intelligence, especially natural language processing (NLP). Recently, inspired by the success of pre-trained language models (PLMs) in the generic domain, many LegalAI researchers devote their effort to apply PLMs to legal tasks. However, utilizing PLMs to address legal tasks is still challenging, as the legal documents usually consist of thousands of tokens, which is far longer than the length that mainstream PLMs can process. In this paper, we release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding. We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering. The experimental results demonstrate that our model can achieve promising improvement on tasks with long documents as inputs.