# MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset

arXiv: https://arxiv.org/abs/2305.04582 · HTML: https://mlsanity.com/paper/2305.04582/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task](https://mlsanity.com/paper/2004.14855/) (2020)
- [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021)
- [Re-TACRED: Addressing Shortcomings of the TACRED Dataset](https://mlsanity.com/paper/2104.08398/) (2021)
- [Translationese as a Language in "Multilingual" NMT](https://mlsanity.com/paper/1911.03823/) (2019)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [Multilingual Extractive Reading Comprehension by Runtime Machine Translation](https://mlsanity.com/paper/1809.03275/) (2018)
- [Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction](https://mlsanity.com/paper/2109.06798/) (2021)
- [Cross-Lingual Machine Reading Comprehension](https://mlsanity.com/paper/1909.00361/) (2019)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification](https://mlsanity.com/paper/2010.09381/) (2020)
- [Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping](https://mlsanity.com/paper/1911.00069/) (2019)
- [Multilingual Relation Extraction using Compositional Universal Schema](https://mlsanity.com/paper/1511.06396/) (2015)
- [DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2104.08655/) (2021)
- [A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification](https://mlsanity.com/paper/2110.09570/) (2021)
- [Multilingual Open Relation Extraction Using Cross-lingual Projection](https://mlsanity.com/paper/1503.06450/) (2015)
- [Transferring Semantic Roles Using Translation and Syntactic Information](https://mlsanity.com/paper/1710.01411/) (2017)
- [Multilingual Relation Classification via Efficient and Effective Prompting](https://mlsanity.com/paper/2210.13838/) (2022)
- [On the Relation between Syntactic Divergence and Zero-Shot Performance](https://mlsanity.com/paper/2110.04644/) (2021)

## Built on by (most cited first, 6 of 6)

- [Contextual Label Projection for Cross-Lingual Structured Prediction](https://mlsanity.com/paper/2309.08943/) (2023)
- [What is "Typological Diversity" in NLP?](https://mlsanity.com/paper/2402.04222/) (2024)
- [KnowCoder-X: Boosting Multilingual Information Extraction via Code](https://mlsanity.com/paper/2411.04794/) (2024)
- [Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer](https://mlsanity.com/paper/2404.16506/) (2024)
- [Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems](https://mlsanity.com/paper/2508.14982/) (2025)
- [Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively](https://mlsanity.com/paper/2610.17703/) (2026)

## Abstract (arXiv)

Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACRED dataset, covering 12 typologically diverse languages from 9 language families, which is created by machine-translating TACRED instances and automatically projecting their entity annotations. We analyze translation and annotation projection quality, identify error categories, and experimentally evaluate fine-tuned pretrained mono- and multilingual language models in common transfer learning scenarios. Our analyses show that machine translation is a viable strategy to transfer RE instances, with native speakers judging more than 83% of the translated instances to be linguistically and semantically acceptable. We find monolingual RE model performance to be comparable to the English original for many of the target languages, and that multilingual models trained on a combination of English and target language data can outperform their monolingual counterparts. However, we also observe a variety of translation and annotation projection errors, both due to the MT systems and linguistic features of the target languages, such as pronoun-dropping, compounding and inflection, that degrade dataset quality and RE model performance.