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Found 7 papers, 2 papers with code
A Survey on Ontology Enrichment from Text
Increased internet bandwidth at low cost is leading to the creation of large volumes of unstructured data.
Code-Switching with Word Senses for Pretraining in Neural Machine Translation
Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022).
Towards Effective Disambiguation for Machine Translation with Large Language Models
Resolving semantic ambiguity has long been recognised as a central challenge in the field of Machine Translation.
The University of Edinburgh's Submission to the WMT22 Code-Mixing Shared Task (MixMT)
For subtask 1 we explored the effects of constrained decoding on English and transliterated subwords in order to produce Hinglish.
A Deep Learning Approach for Ontology Enrichment from Unstructured Text
Information Security in the cyber world is a major cause for concern, with a significant increase in the number of attack surfaces.
VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment
Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc.
Multifaceted Context Representation using Dual Attention for Ontology Alignment
Ontology Alignment is an important research problem that finds application in various fields such as data integration, data transfer, data preparation etc.
