# Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping

arXiv: https://arxiv.org/abs/1911.00069 · HTML: https://mlsanity.com/paper/1911.00069/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction](https://mlsanity.com/paper/2203.08308/) (2022)
- [Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment](https://mlsanity.com/paper/2101.11112/) (2021)
- [DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2104.08655/) (2021)
- [Contextual Label Projection for Cross-Lingual Structured Prediction](https://mlsanity.com/paper/2309.08943/) (2023)
- [MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset](https://mlsanity.com/paper/2305.04582/) (2023)
- [Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction](https://mlsanity.com/paper/2305.12258/) (2023)
- [A Generative Model for Relation Extraction and Classification](https://mlsanity.com/paper/2202.13229/) (2022)
- [Prompt-Learning for Cross-Lingual Relation Extraction](https://mlsanity.com/paper/2304.10354/) (2023)
- [SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot Cross-lingual Information Extraction](https://mlsanity.com/paper/2305.12389/) (2023)
- [MixRED: A Mix-lingual Relation Extraction Dataset](https://mlsanity.com/paper/2403.15696/) (2024)
- [DISCIE -- Discriminative Closed Information Extraction](https://mlsanity.com/paper/2506.16348/) (2025)

## Abstract (arXiv)

Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challenging to transfer an RE model of a resource-rich language to a resource-poor language. In this paper, we propose a new approach for cross-lingual RE model transfer based on bilingual word embedding mapping. It projects word embeddings from a target language to a source language, so that a well-trained source-language neural network RE model can be directly applied to the target language. Experiment results show that the proposed approach achieves very good performance for a number of target languages on both in-house and open datasets, using a small bilingual dictionary with only 1K word pairs.