ParaLaw Nets -- Cross-lingual Sentence-level Pretraining for Legal Text Processing
This addresses ambiguity reduction for legal text processing, but it appears incremental as it builds on existing pretraining methods with a domain-specific focus.
The authors tackled ambiguity in legal text processing by proposing ParaLaw Nets, a pretrained model family using sentence-level cross-lingual information, which achieved the best result in the Question Answering task of COLIEE-2021.
Ambiguity is a characteristic of natural language, which makes expression ideas flexible. However, in a domain that requires accurate statements, it becomes a barrier. Specifically, a single word can have many meanings and multiple words can have the same meaning. When translating a text into a foreign language, the translator needs to determine the exact meaning of each element in the original sentence to produce the correct translation sentence. From that observation, in this paper, we propose ParaLaw Nets, a pretrained model family using sentence-level cross-lingual information to reduce ambiguity and increase the performance in legal text processing. This approach achieved the best result in the Question Answering task of COLIEE-2021.