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

Bhavyajeet Singh, Pavan Kandru, Anubhav Sharma, Vasudeva Varma
2/9/2023
cs.CLcs.AIcs.IR

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Abstract

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.

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Cite this paper

@article{singh2023massively,
  title  = {Massively Multilingual Language Models for Cross Lingual Fact Extraction from Low Resource Indian Languages},
  author = {Bhavyajeet Singh and Pavan Kandru and Anubhav Sharma and Vasudeva Varma},
  year   = {2023},
  eprint = {2302.04790},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.04790v1}
}

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