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Emotion Enriched Retrofitted Word Embeddings
The retrofitted embeddings achieve better inter-cluster and intra-cluster distance for words having the same emotions, e. g., the joy cluster containing words like fun, happiness, etc., and the anger cluster with words like offence, rage, etc., as evaluated through different cluster quality metrics.
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning
We present a novel retrofitting method to induce emotion aspects into pre-trained language models (PLMs) such as BERT and RoBERTa.
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings
We present a novel retrofitting model that can leverage relational knowledge available in a knowledge resource to improve word embeddings.
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