  False claims that have been previously fact-checked can still spread on
social media. To mitigate their continual spread, detecting previously
fact-checked claims is indispensable. Given a claim, existing works focus on
providing evidence for detection by reranking candidate fact-checking articles
(FC-articles) retrieved by BM25. However, these performances may be limited
because they ignore the following characteristics of FC-articles: (1) claims
are often quoted to describe the checked events, providing lexical information
besides semantics; (2) sentence templates to introduce or debunk claims are
common across articles, providing pattern information. Models that ignore the
two aspects only leverage semantic relevance and may be misled by sentences
that describe similar but irrelevant events. In this paper, we propose a novel
reranker, MTM (Memory-enhanced Transformers for Matching) to rank FC-articles
using key sentences selected with event (lexical and semantic) and pattern
information. For event information, we propose a ROUGE-guided Transformer which
is finetuned with regression of ROUGE. For pattern information, we generate
pattern vectors for matching with sentences. By fusing event and pattern
information, we select key sentences to represent an article and then predict
if the article fact-checks the given claim using the claim, key sentences, and
patterns. Experiments on two real-world datasets show that MTM outperforms
existing methods. Human evaluation proves that MTM can capture key sentences
for explanations. The code and the dataset are at
https://github.com/ICTMCG/MTM.
