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Found 7 papers, 3 papers with code
Vision-and-Language Pretraining
Inheriting the spirit of Transfer Learning, research works in V&L have devised multiple pretraining techniques on large-scale datasets in order to enhance the performance of downstream tasks.
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach.
Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge Distillation
Current state-of-the-art cross-lingual summarization models employ multi-task learning paradigm, which works on a shared vocabulary module and relies on the self-attention mechanism to attend among tokens in two languages.
Contrastive Learning for Neural Topic Model
Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by differentiating a document with another dissimilar sample.
Enriching and Controlling Global Semantics for Text Summarization
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries.
Adaptive Name Entity Recognition under Highly Unbalanced Data
For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize entities in text into predefined groups such as the names of persons, locations, quantities, organizations or percentages, etc.
Fast Transient Simulation of High-Speed Channels Using Recurrent Neural Network
In this paper, we leverage machine learning methods, to be specific, the recurrent neural network (RNN), to generate black-box macromodels and achieve significant reduction of computation time.
