Finding Already Debunked Narratives via Multistage Retrieval: Enabling
  Cross-Lingual, Cross-Dataset and Zero-Shot Learning
Iknoor Singh Carolina Scarton Xingyi Song Kalina Bontcheva
The task of retrieving already debunked narratives aims to detect stories
that have already been fact-checked. The successful detection of claims that
have already been debunked not only reduces the manual efforts of professional
fact-checkers but can also contribute to slowing the spread of misinformation.
Mainly due to the lack of readily available data, this is an understudied
problem, particularly when considering the cross-lingual task, i.e. the
retrieval of fact-checking articles in a language different from the language
of the online post being checked. This paper fills this gap by (i) creating a
novel dataset to enable research on cross-lingual retrieval of already debunked
narratives, using tweets as queries to a database of fact-checking articles;
(ii) presenting an extensive experiment to benchmark fine-tuned and
off-the-shelf multilingual pre-trained Transformer models for this task; and
(iii) proposing a novel multistage framework that divides this cross-lingual
debunk retrieval task into refinement and re-ranking stages. Results show that
the task of cross-lingual retrieval of already debunked narratives is
challenging and off-the-shelf Transformer models fail to outperform a strong
lexical-based baseline (BM25). Nevertheless, our multistage retrieval framework
is robust, outperforming BM25 in most scenarios and enabling cross-domain and
zero-shot learning, without significantly harming the model's performance.