To participate in the Isometric Spoken Language Translation Task of the IWSLT
2022 evaluation, constrained condition, AppTek developed neural
Transformer-based systems for English-to-German with various mechanisms of
length control, ranging from source-side and target-side pseudo-tokens to
encoding of remaining length in characters that replaces positional encoding.
We further increased translation length compliance by sentence-level selection
of length-compliant hypotheses from different system variants, as well as
rescoring of N-best candidates from a single system. Length-compliant
back-translated and forward-translated synthetic data, as well as other
parallel data variants derived from the original MuST-C training corpus were
important for a good quality/desired length trade-off. Our experimental results
show that length compliance levels above 90% can be reached while minimizing
losses in MT quality as measured in BERT and BLEU scores.

AppTek 使用基于神经 Transformer 的系统对英语到德语的音–语翻译任务进行了改进，通过使用源端伪标记、目标端伪标记和使用剩余字符长度编码来实现长度控制，同时使用由 MuST-C 原始语料库派生的合成数据，使翻译满足长度条件，研究结果表明，可以在最小化 BERT 和 BLEU 分数损失的同时实现超过 90％的长度符合度。