# Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language

arXiv: https://arxiv.org/abs/2403.17143 · HTML: https://mlsanity.com/paper/2403.17143/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 25 Mar 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy](https://mlsanity.com/paper/1909.04273/) (2019)
- [OneRel:Joint Entity and Relation Extraction with One Module in One Step](https://mlsanity.com/paper/2203.05412/) (2022)
- [Improving Relation Extraction by Pre-trained Language Representations](https://mlsanity.com/paper/1906.03088/) (2019)
- [Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text](https://mlsanity.com/paper/1908.07721/) (2019)
- [Pantheon 1.0, a manually verified dataset of globally famous biographies](https://mlsanity.com/paper/1502.07310/) (2015)
- [Biographical: A Semi-Supervised Relation Extraction Dataset](https://mlsanity.com/paper/2205.00806/) (2022)
- [Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding](https://mlsanity.com/paper/1812.09516/) (2018)

## Built on by (most cited first, 3 of 3)

- [LLM-based Embedders for Prior Case Retrieval](https://mlsanity.com/paper/2507.18455/) (2025)
- [CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts](https://mlsanity.com/paper/2602.17663/) (2026)
- [DistilledGemma: Balanced Efficiency-Accuracy for Person-Place Relation Extraction from Multilingual Historical Articles](https://mlsanity.com/paper/2606.29130/) (2026)

## Abstract (arXiv)

Relation extraction is essential for extracting and understanding biographical information in the context of digital humanities and related subjects. There is a growing interest in the community to build datasets capable of training machine learning models to extract relationships. However, annotating such datasets can be expensive and time-consuming, in addition to being limited to English. This paper applies guided distant supervision to create a large biographical relationship extraction dataset for German. Our dataset, composed of more than 80,000 instances for nine relationship types, is the largest biographical German relationship extraction dataset. We also create a manually annotated dataset with 2000 instances to evaluate the models and release it together with the dataset compiled using guided distant supervision. We train several state-of-the-art machine learning models on the automatically created dataset and release them as well. Furthermore, we experiment with multilingual and cross-lingual experiments that could benefit many low-resource languages.