Search Results for author:
Found 7 papers, 3 papers with code
GPT-DETOX: An In-Context Learning-Based Paraphraser for Text Detoxification
In this paper, we propose GPT-DETOX as a framework for prompt-based in-context learning for text detoxification using GPT-3. 5 Turbo.
RECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?
We initialize the weights of the entities with these embeddings to train our knowledge graph embedding (KGE) model.
Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval
Experimental results show that Late Fusion contrastive learning for Neural RIR outperforms all other contrastive IR configurations, Neural IR, and sparse retrieval baselines, thus demonstrating the power of exploiting the two-level structure in Neural RIR approaches as well as the importance of preserving the nuance of individual review content via Late Fusion methods.
DiffuDetox: A Mixed Diffusion Model for Text Detoxification
Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text.
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports.
McDiarmid Drift Detection Methods for Evolving Data Streams
Accordingly, concept drifts need to be detected, and handled, as soon as possible.
Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
In addition, a number of methods have been developed to detect concept drifts in these streams.
