Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Matching Tweets With Applicable Fact-Checks Across Languages   An important challenge for news fact-checking is the effective dissemination
of existing fact-checks. This in turn brings the need for reliable methods to
detect previously fact-checked claims. In this paper, we focus on automatically
finding existing fact-checks for claims made in social media posts (tweets). We
conduct both classification and retrieval experiments, in monolingual (English
only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English)
settings using multilingual transformer models such as XLM-RoBERTa and
multilingual embeddings such as LaBSE and SBERT. We present promising results
for "match" classification (93% average accuracy) in four language pairs. We
also find that a BM25 baseline outperforms state-of-the-art multilingual
embedding models for the retrieval task during our monolingual experiments. We
highlight and discuss NLP challenges while addressing this problem in different
languages, and we introduce a novel curated dataset of fact-checks and
corresponding tweets for future research.

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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn