Spoken language glossification (SLG) aims to translate the spoken language
text into the sign language gloss, i.e., a written record of sign language. In
this work, we present a framework named $S$emi-$S$upervised $S$poken $L$anguage
$G$lossification ($S^3$LG) for SLG. To tackle the bottleneck of limited
parallel data in SLG, our $S^3$LG incorporates large-scale monolingual spoken
language text into SLG training. The proposed framework follows the
self-training structure that iteratively annotates and learns from pseudo
labels. Considering the lexical similarity and syntactic difference between
sign language and spoken language, our $S^3$LG adopts both the rule-based
heuristic and model-based approach for auto-annotation. During training, we
randomly mix these complementary synthetic datasets and mark their differences
with a special token. As the synthetic data may be less quality, the $S^3$LG
further leverages consistency regularization to reduce the negative impact of
noise in the synthetic data. Extensive experiments are conducted on public
benchmarks to demonstrate the effectiveness of the $S^3$LG. Our code is
available at https://github.com/yaohj11/S3LG.

半监督口语语言标注（S3LG）框架通过引入大规模单语口语文本到标注的手语编码训练中，采用基于规则和基于模型的自动标注方法，添加一种特殊标记以区分合成数据，并利用一致性正则化减少合成数据中的噪音负面影响，通过在公共基准测试上的大量实验证明了该框架的有效性。