# Massively Multilingual Language Models for Cross Lingual Fact Extraction from Low Resource Indian Languages

arXiv: https://arxiv.org/abs/2302.04790 · HTML: https://mlsanity.com/paper/2302.04790/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Text Generation from Knowledge Graphs with Graph Transformers](https://mlsanity.com/paper/1904.02342/) (2019)
- [Joint Entity and Relation Extraction with Set Prediction Networks](https://mlsanity.com/paper/2011.01675/) (2020)
- [CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning](https://mlsanity.com/paper/1911.10438/) (2019)
- [A Partition Filter Network for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2108.12202/) (2021)
- [Knowledge Graph Generation From Text](https://mlsanity.com/paper/2211.10511/) (2022)
- [Multilingual Fact Linking](https://mlsanity.com/paper/2109.14364/) (2021)
- [Improving Question Answering over Knowledge Graphs Using Graph Summarization](https://mlsanity.com/paper/2203.13570/) (2022)
- [XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages](https://mlsanity.com/paper/2202.00291/) (2022)

## Built on by (most cited first, 1 of 1)

- [In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs](https://mlsanity.com/paper/2605.05931/) (2026)

## Abstract (arXiv)

Massive knowledge graphs like Wikidata attempt to capture world knowledge about multiple entities. Recent approaches concentrate on automatically enriching these KGs from text. However a lot of information present in the form of natural text in low resource languages is often missed out. Cross Lingual Information Extraction aims at extracting factual information in the form of English triples from low resource Indian Language text. Despite its massive potential, progress made on this task is lagging when compared to Monolingual Information Extraction. In this paper, we propose the task of Cross Lingual Fact Extraction(CLFE) from text and devise an end-to-end generative approach for the same which achieves an overall F1 score of 77.46.