# Controlling the Output Length of Neural Machine Translation

arXiv: https://arxiv.org/abs/1910.10408 · HTML: https://mlsanity.com/paper/1910.10408/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 30 of 36)

- [PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation](https://mlsanity.com/paper/2205.12206/) (2022)
- [VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing](https://mlsanity.com/paper/2211.16934/) (2022)
- [Machine Translation Verbosity Control for Automatic Dubbing](https://mlsanity.com/paper/2110.03847/) (2021)
- [Isometric MT: Neural Machine Translation for Automatic Dubbing](https://mlsanity.com/paper/2112.08682/) (2021)
- [Isochrony-Aware Neural Machine Translation for Automatic Dubbing](https://mlsanity.com/paper/2112.08548/) (2021)
- [Fine-Tuning Transformers: Vocabulary Transfer](https://mlsanity.com/paper/2112.14569/) (2021)
- [Automatic Song Translation for Tonal Languages](https://mlsanity.com/paper/2203.13420/) (2022)
- [Songs Across Borders: Singable and Controllable Neural Lyric Translation](https://mlsanity.com/paper/2305.16816/) (2023)
- [RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation](https://mlsanity.com/paper/2305.17131/) (2023)
- [Direct Speech Translation for Automatic Subtitling](https://mlsanity.com/paper/2209.13192/) (2022)
- [Prosodic Alignment for off-screen automatic dubbing](https://mlsanity.com/paper/2204.02530/) (2022)
- [BERT in Plutarch's Shadows](https://mlsanity.com/paper/2211.05673/) (2022)
- [Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics](https://mlsanity.com/paper/2303.15705/) (2023)
- [Towards Personalised and Document-level Machine Translation of Dialogue](https://mlsanity.com/paper/2102.10979/) (2021)
- [Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022](https://mlsanity.com/paper/2205.05990/) (2022)
- [Evaluating Subtitle Segmentation for End-to-end Generation Systems](https://mlsanity.com/paper/2205.09360/) (2022)
- [Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning](https://mlsanity.com/paper/2403.15469/) (2024)
- [Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation](https://mlsanity.com/paper/2107.13689/) (2021)
- [Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation](https://mlsanity.com/paper/2205.04747/) (2022)
- [AppTek's Submission to the IWSLT 2022 Isometric Spoken Language Translation Task](https://mlsanity.com/paper/2205.05807/) (2022)
- [Sockeye 3: Fast Neural Machine Translation with PyTorch](https://mlsanity.com/paper/2207.05851/) (2022)
- [Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization](https://mlsanity.com/paper/2508.08550/) (2025)
- [Controlling keywords and their positions in text generation](https://mlsanity.com/paper/2304.09516/) (2023)
- [MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation](https://mlsanity.com/paper/2305.15904/) (2023)
- [How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?](https://mlsanity.com/paper/2309.08565/) (2023)
- [Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines](https://mlsanity.com/paper/2506.01265/) (2025)
- [Prompting LLMs: Length Control for Isometric Machine Translation](https://mlsanity.com/paper/2506.04855/) (2025)
- [An Empirical Study of Extrapolation in Text Generation with Scalar Control](https://mlsanity.com/paper/2104.07910/) (2021)
- [Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation](https://mlsanity.com/paper/2307.02146/) (2023)
- [Controllable Image Captioning](https://mlsanity.com/paper/2204.13324/) (2022)

## Abstract (arXiv)

The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted. In particular, if translations have to fit some given layout, quality should not only be measured in terms of adequacy and fluency, but also length. Exemplary cases are the translation of document files, subtitles, and scripts for dubbing, where the output length should ideally be as close as possible to the length of the input text. This paper addresses for the first time, to the best of our knowledge, the problem of controlling the output length in NMT. We investigate two methods for biasing the output length with a transformer architecture: i) conditioning the output to a given target-source length-ratio class and ii) enriching the transformer positional embedding with length information. Our experiments show that both methods can induce the network to generate shorter translations, as well as acquiring interpretable linguistic skills.