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Found 8 papers, 1 papers with code
A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions
Our study demonstrates that the copy mechanism yields significant performance enhancements for most PLMs and NPLMs.
A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation
However, current architectures are unable to integrate the knowledge base (KB) schema and handle questions on knowledge resources, classes, and properties unseen during training, rendering them unusable outside the scope of topics covered in the training set.
Local Structure Matters Most in Most Languages
In this work, we replicate a study on the importance of local structure, and the relative unimportance of global structure, in a multilingual setting.
Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes
However, this transfer is not universal, with many languages not currently understood by multilingual approaches.
Toward a traceable, explainable, and fairJD/Resume recommendation system
Typically, pre-trained language models use transfer-based machine learning models to be fine-tuned for a specific field.
Local Structure Matters Most: Perturbation Study in NLU
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models are surprisingly insensitive to the order of words.
MLMLM: Link Prediction with Mean Likelihood Masked Language Model
They however scale with man-hours and high-quality data.
Ranked #11 on
Link Prediction
on WN18RR
(using extra training data)
Ontology Matching Using Convolutional Neural Networks
In order to achieve interoperability of information in the context of the Semantic Web, it is necessary to find effective ways to align different ontologies.
