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We report a wide range of experimental results for various Transformer setups and introduce the use of Spatial-Temporal Multi-Cue (STMC) networks in an end-to-end SLT system with Transformer. We perform experiments on RWTH-PHOENIX-Weather 2014T, a challenging SLT benchmark dataset of German sign language, and ASLG-PC12, a dataset involving American Sign Language (ASL) recently used in gloss-to-text translation. Our methodology improves on the current state-of-the-art by over 5 and 7 points respectively in BLEU-4 score on ground truth glosses and by using an STMC network to predict glosses of the RWTH-PHOENIX-Weather 2014T dataset. On the ASLG-PC12 corpus, we report an improvement of over 16 points in BLEU-4. Our findings also demonstrate that end-to-end translation on predicted glosses provides even better performance than translation on ground truth glosses. |
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