# DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation

arXiv: https://arxiv.org/abs/2504.05122 · HTML: https://mlsanity.com/paper/2504.05122/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks](https://mlsanity.com/paper/1906.01017/) (2019)
- [FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation](https://mlsanity.com/paper/1912.00941/) (2019)
- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training](https://mlsanity.com/paper/2308.07934/) (2023)
- [HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving](https://mlsanity.com/paper/2411.17530/) (2024)
- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective](https://mlsanity.com/paper/2412.03630/) (2024)
- [Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving](https://mlsanity.com/paper/2411.17543/) (2024)
- [Designing DNNs for a trade-off between robustness and processing performance in embedded devices](https://mlsanity.com/paper/2412.03682/) (2024)
- [Transient Fault Tolerant Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2408.16952/) (2024)

## Built on by (most cited first, 2 of 2)

- [M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation](https://mlsanity.com/paper/2510.13434/) (2025)
- [Graph-Based Phonetic Error Correction of Noisy ASR](https://mlsanity.com/paper/2606.24889/) (2026)

## Abstract (arXiv)

Document-level context is crucial for handling discourse challenges in text-to-text document-level machine translation (MT). Despite the increased discourse challenges introduced by noise from automatic speech recognition (ASR), the integration of document-level context in speech translation (ST) remains insufficiently explored. In this paper, we develop DoCIA, an online framework that enhances ST performance by incorporating document-level context. DoCIA decomposes the ST pipeline into four stages. Document-level context is integrated into the ASR refinement, MT, and MT refinement stages through auxiliary LLM (large language model)-based modules. Furthermore, DoCIA leverages document-level information in a multi-level manner while minimizing computational overhead. Additionally, a simple yet effective determination mechanism is introduced to prevent hallucinations from excessive refinement, ensuring the reliability of the final results. Experimental results show that DoCIA significantly outperforms traditional ST baselines in both sentence and discourse metrics across four LLMs, demonstrating its effectiveness in improving ST performance.