  Knowledge-intensive NLP tasks can benefit from linking natural language text
with facts from a Knowledge Graph (KG). Although facts themselves are
language-agnostic, the fact labels (i.e., language-specific representation of
the fact) in the KG are often present only in a few languages. This makes it
challenging to link KG facts to sentences in languages other than the limited
set of languages. To address this problem, we introduce the task of
Multilingual Fact Linking (MFL) where the goal is to link fact expressed in a
sentence to corresponding fact in the KG, even when the fact label in the KG is
not available in the language of the sentence. To facilitate research in this
area, we present a new evaluation dataset, IndicLink. This dataset contains
11,293 linked WikiData facts and 6,429 sentences spanning English and six
Indian languages. We propose a Retrieval+Generation model, ReFCoG, that can
scale to millions of KG facts by combining Dual Encoder based retrieval with a
Seq2Seq based generation model which is constrained to output only valid KG
facts. ReFCoG outperforms standard Retrieval+Re-ranking models by 10.7 pts in
Precision@1. In spite of this gain, the model achieves an overall score of
52.1, showing ample scope for improvement in the task.ReFCoG code and IndicLink
data are available at https://github.com/SaiKeshav/mfl
