  This work dedicates to continuous sign language recognition (CSLR), which is
a weakly supervised task dealing with the recognition of continuous signs from
videos, without any prior knowledge about the temporal boundaries between
consecutive signs. Data scarcity heavily impedes the progress of CSLR. Existing
approaches typically train CSLR models on a monolingual corpus, which is orders
of magnitude smaller than that of speech recognition. In this work, we explore
the feasibility of utilizing multilingual sign language corpora to facilitate
monolingual CSLR. Our work is built upon the observation of cross-lingual
signs, which originate from different sign languages but have similar visual
signals (e.g., hand shape and motion). The underlying idea of our approach is
to identify the cross-lingual signs in one sign language and properly leverage
them as auxiliary training data to improve the recognition capability of
another. To achieve the goal, we first build two sign language dictionaries
containing isolated signs that appear in two datasets. Then we identify the
sign-to-sign mappings between two sign languages via a well-optimized isolated
sign language recognition model. At last, we train a CSLR model on the
combination of the target data with original labels and the auxiliary data with
mapped labels. Experimentally, our approach achieves state-of-the-art
performance on two widely-used CSLR datasets: Phoenix-2014 and Phoenix-2014T.
