  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.
