# AppTek's Submission to the IWSLT 2022 Isometric Spoken Language Translation Task

arXiv: https://arxiv.org/abs/2205.05807 · HTML: https://mlsanity.com/paper/2205.05807/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Positional Encoding to Control Output Sequence Length](https://mlsanity.com/paper/1904.07418/) (2019)
- [Controlling the Output Length of Neural Machine Translation](https://mlsanity.com/paper/1910.10408/) (2019)
- [RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition](https://mlsanity.com/paper/1805.05225/) (2018)
- [Machine Translation Verbosity Control for Automatic Dubbing](https://mlsanity.com/paper/2110.03847/) (2021)
- [RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks](https://mlsanity.com/paper/1608.00895/) (2016)
- [Adapting End-to-End Speech Recognition for Readable Subtitles](https://mlsanity.com/paper/2005.12143/) (2020)
- [Machine Translation with Unsupervised Length-Constraints](https://mlsanity.com/paper/2004.03176/) (2020)
- [Novel Applications of Factored Neural Machine Translation](https://mlsanity.com/paper/1910.03912/) (2019)

## Built on by (most cited first, 3 of 3)

- [Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters](https://mlsanity.com/paper/2305.13204/) (2023)
- [Prompting LLMs: Length Control for Isometric Machine Translation](https://mlsanity.com/paper/2506.04855/) (2025)
- [Not Quite My Tempo: Voice Activity-aware Speech Synthesis for Lip-Synchronous Dubbing](https://mlsanity.com/paper/2609.26486/) (2026)

## Abstract (arXiv)

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.