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Found 6 papers, 2 papers with code
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3
This paper presents a modified version of the sentence paraphrasing deep learning model (PEGASUS) to tackle the limitation of maximum sequence length.
Quantum Semi-Supervised Kernel Learning
While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels.
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices
Language models employ a very large number of trainable parameters.
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task
For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93. 7%) among 27 participants with very competitive results.
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement
Our approach achieves a hundred-fold or more reduction in the space required to store the embeddings with almost no relative drop in accuracy in practical natural language processing tasks.
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs
When samples have internal structure, we often see a mismatch between the objective optimized during training and the model's goal during inference.
