  In this paper, we present our methodologies for tackling the challenges of
legal case retrieval and entailment in the Competition on Legal Information
Extraction / Entailment 2020 (COLIEE-2020). We participated in the two case law
tasks, i.e., the legal case retrieval task and the legal case entailment task.
Task 1 (the retrieval task) aims to automatically identify supporting cases
from the case law corpus given a new case, and Task 2 (the entailment task) to
identify specific paragraphs that entail the decision of a new case in a
relevant case. In both tasks, we employed the neural models for semantic
understanding and the traditional retrieval models for exact matching. As a
result, our team (TLIR) ranked 2nd among all of the teams in Task 1 and 3rd
among teams in Task 2. Experimental results suggest that combing models of
semantic understanding and exact matching benefits the legal case retrieval
task while the legal case entailment task relies more on semantic
understanding.
