# Improved Long-Form Spoken Language Translation with Large Language Models

arXiv: https://arxiv.org/abs/2212.09895 · HTML: https://mlsanity.com/paper/2212.09895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [On NMT Search Errors and Model Errors: Cat Got Your Tongue?](https://mlsanity.com/paper/1908.10090/) (2019)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [SHAS: Approaching optimal Segmentation for End-to-End Speech Translation](https://mlsanity.com/paper/2202.04774/) (2022)
- [Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation](https://mlsanity.com/paper/1409.1257/) (2014)
- [SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech Translation](https://mlsanity.com/paper/2002.12231/) (2020)
- [Sentence Boundary Augmentation For Neural Machine Translation Robustness](https://mlsanity.com/paper/2010.11132/) (2020)
- [Replacing Human Audio with Synthetic Audio for On-device Unspoken Punctuation Prediction](https://mlsanity.com/paper/2010.10203/) (2020)

## Built on by (most cited first, 1 of 1)

- [Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR](https://mlsanity.com/paper/2305.18419/) (2023)

## Abstract (arXiv)

A challenge in spoken language translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations. To address this mismatch, we fine-tune a general-purpose, large language model to split long ASR transcripts into segments that can be independently translated so as to maximize the overall translation quality. We compare to several segmentation strategies and find that our approach improves BLEU score on three languages by an average of 2.7 BLEU overall compared to an automatic punctuation baseline. Further, we demonstrate the effectiveness of two constrained decoding strategies to improve well-formedness of the model output from above 99% to 100%.