Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction

Elisa Bassignana, Filip Ginter, Sampo Pyysalo, Rob van der Goot, Barbara Plank
5/18/2023
cs.CL

No official code found. Best implementation: mainlp/CrossRE (49 stars, GPL-3.0, last commit 2024-08-20).

Abstract

Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. Multi-CrossRE is a machine translated version of CrossRE (Bassignana and Plank, 2022), with a sub-portion including more than 200 sentences in seven diverse languages checked by native speakers. We run a baseline model over the 26 new datasets and--as sanity check--over the 26 back-translations to English. Results on the back-translated data are consistent with the ones on the original English CrossRE, indicating high quality of the translation and the resulting dataset.

View on arXivView PDF

Code Implementations(1)

mainlp/CrossRECommunity100%
GPL-3.0

CrossRE: A Cross-Domain Dataset for Relation Extraction (Findings of EMNLP 2022)

490Shell, PythonOct 15, 20222 years agoGPL-3.0

Similar Papers

The Thiomi Dataset: A Large-Scale Multimodal Corpus for Low-Resource African Languages

87% match2026

IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages

87% match1 repo4762023

IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages

87% match1 repo42023

Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages

87% match1 repo82023

Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM

86% match1 repo112023

Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages

86% match2023

Cite this paper

@article{bassignana2023multicrossre,
  title  = {Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction},
  author = {Elisa Bassignana and Filip Ginter and Sampo Pyysalo and Rob van der Goot and Barbara Plank},
  year   = {2023},
  eprint = {2305.10985},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.10985v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2305.10985.svg)](https://codeofpaper.com/paper/2305.10985)

Discussion