# SeaAlert: Robust Severity Classification and LLM-Based Information Extraction for Noisy Maritime Distress Communications

arXiv: https://arxiv.org/abs/2604.14163 · HTML: https://mlsanity.com/paper/2604.14163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 17 of 17)

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- [On the Lack of Robust Interpretability of Neural Text Classifiers](https://mlsanity.com/paper/2106.04631/) (2021)
- [HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](https://mlsanity.com/paper/2303.06095/) (2023)
- [Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention](https://mlsanity.com/paper/2311.17400/) (2023)
- [Resolving the Scope of Speculation and Negation using Transformer-Based Architectures](https://mlsanity.com/paper/2001.02885/) (2020)
- [Noisy Text Data: Achilles' Heel of popular transformer based NLP models](https://mlsanity.com/paper/2110.03353/) (2021)
- [Developmental Negation Processing in Transformer Language Models](https://mlsanity.com/paper/2204.14114/) (2022)

## Abstract (arXiv)

Maritime distress communications transmitted over very high frequency (VHF) radio are safety-critical voice messages used to report emergencies at sea. Under the Global Maritime Distress and Safety System (GMDSS), such messages follow standardized procedures and are expected to convey essential details, including vessel identity, position, nature of the distress, and required assistance. In practice, however, automatic analysis remains difficult because distress messages are often brief, noisy, and produced under stress, may deviate from the prescribed format, and are further degraded by automatic speech recognition (ASR) errors caused by channel noise and speaker stress. This paper presents SeaAlert, a controlled experimental framework for evaluating robust analysis of maritime distress communications using transformer-based severity classification and LLM-based structured extraction. To address the scarcity of labeled real-world data, we develop a synthetic data generation pipeline in which an LLM produces diverse maritime messages, including challenging variants in which standard distress codewords are omitted or replaced with less explicit expressions. The generated utterances are synthesized into speech, degraded with simulated VHF noise, and transcribed by an ASR system to obtain controlled noise-degraded transcripts. The resulting evaluation shows that transformer-based classification degrades more gracefully than lexical baselines under ASR noise and codeword masking, while LLM-based extraction is more effective than Regex-based extraction for noisy structured fields.