REVIEW 1 cited by
Massively Multilingual Language Models for Cross Lingual Fact Extraction from Low Resource Indian Languages
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs
A research plan to analyze language distribution in LOD knowledge graphs and explore cross-lingual transfer plus analogical reasoning to improve coverage for low-resource languages.
Discussion (0). Sign in to comment.