# MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base

arXiv: https://arxiv.org/abs/2207.13929 · HTML: https://mlsanity.com/paper/2207.13929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://mlsanity.com/paper/1907.12412/) (2019)
- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)
- [Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model](https://mlsanity.com/paper/1912.09637/) (2019)
- [CoLAKE: Contextualized Language and Knowledge Embedding](https://mlsanity.com/paper/2010.00309/) (2020)
- [ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning](https://mlsanity.com/paper/2012.15022/) (2020)
- [Integrating Graph Contextualized Knowledge into Pre-trained Language Models](https://mlsanity.com/paper/1912.00147/) (2019)

## Abstract (arXiv)

Incorporating structured knowledge into pre-trained language models has demonstrated signiffcant bene-ffts for domain-speciffc natural language processing tasks, particularly in specialized ffelds like military intelligence analysis. Existing approaches typically integrate external knowledge through masking tech-niques or fusion mechanisms, but often fail to fully leverage the intrinsic tactical associations and factual information within input sequences, while introducing uncontrolled noise from unveriffed exter-nal sources. To address these limitations, we present MLRIP (Military Language Representation with Integrated Prior), a novel pre-training framework that introduces a hierarchical knowledge integration pipeline combined with a dual-phase entity substitu-tion mechanism. Our approach speciffcally models operational linkages between military entities, capturing critical dependencies such as command, support, and engagement structures. Comprehensive evaluations on military-speciffc NLP tasks show that MLRIP outperforms existing BERT-based models by substantial margins, establishing new state-of-the-art performance in military entity recognition, typing, and operational linkage extraction tasks while demonstrating superior operational efffciency in resource-constrained environments.