Relation Extraction using Few-Shot Entailment on Conversational Data
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2023-03-17
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en
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Abstract
Relation Extraction is an important task for personal Natural Language Understanding,
and especially so in conversational applications where Knowledge Graphs, built from
such relations, are essential for knowledge storage. Recent advances in Natural Language
Understanding have shown that pre-trained Language Models tend to be the best at
solving various Language Understanding tasks. Most of the time, they are fine-tuned on
the specific downstream task, which relies on a large enough, high-quality dataset for the
task. To overcome this issue and improve model efficiency, the novel approach of Few-Shot
Learning is explored. Combined with the task of Natural Language Entailment, recent
research has shown that models using Few-Shot Learning and Inference can outperform
fully fine-tuned State-of-the-Art models on Relation Extraction tasks (and others). Since
relations are so important for conversational applications, the question is in how far the
approach of performing Relation Extraction with Entailment-based Few-Shot Learning
can be applied to conversational domains. Therefore, this thesis investigates in how far
this is applicable. The obtained results of the Few-Shot Entailment models tested do
not reach state-of-the-art approaches on relatively comparable tasks. Still, one main
conclusion is that entity type information is potentially an important factor for accurate
relation extraction, which is recommended for further research.
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Faculteit der Sociale Wetenschappen