Paper classifies multiple video sources in encrypted tunnels using NLP-inspired features.
problem Traffic classification in encrypted video streams.
method Deep learning with a novel NLP-inspired feature for multi-label classification.
result The method achieves high performance on binary and multilabel classification tasks.
Self-knowledge distillation improves NLP performance.
problem Efficient knowledge representation in NLP models.
method A new knowledge distillation method using soft target probabilities from the training model itself.
result Improves performance on language model and neural machine translation tasks.
MDA optimizer performs similarly to SGD+M in CV and Adam in NLP.
problem Performance degradation due to choosing the wrong optimizer.
method Modernized Dual Averaging (MDA) optimizer, inspired by dual averaging.
result MDA performs as well as SGD+M in CV and as Adam in NLP.
The paper improves cryptocurrency price forecasting using deep learning and NLP on financial, blockchain, and social media data.
problem Improving cryptocurrency price forecasting accuracy and profitability.
method Integrates financial, blockchain, and social media data; applies BART MNLI model for sentiment analysis; uses deep learning NLP models; compares with traditional methods; uses local extrema as predictive targets.
result Significantly improves forecasting accuracy and profitability of cryptocurrency price predictions.
Model generates label-dependent paraphrases for NLP tasks.
problem Generating semantically different paraphrases for NLP tasks.
method Deep variational model with label-dependent generation.
result Model improves generative power of paraphrasing models.
Survey on using large models to train smaller datasets in NLP.
problem Lack of large datasets and computing resources for NLP tasks.
method Analysis of recent transfer learning approaches in NLP.
result Increased demand for transfer learning in NLP due to large models.
New method learns latent structures for deep NLP models without tradeoffs.
problem Joint learning of latent structures and downstream predictors with end-to-end differentiability.
method SparseMAP inference for joint learning of latent structures and downstream predictors.
result First method to enable unrestricted dynamic computation graph construction from global latent structure while maintaining differentiability.
The paper predicts stock trends from news headlines using NLP.
problem Predicting stock trends from news headlines.
method Statistical and deep learning models with NLP techniques.
result Improved accuracy in predicting DJIA trends.
Deep neural networks blend mechanistic and phenomenological NLP approaches.
problem Combining theory-driven and data-driven NLP methods.
method Using deep neural networks to integrate mechanistic and phenomenological models.
result Deep learning can effectively model language and perception in spatial cognition.
A2 Learning reduces redundant examples in AL for NLP tasks.
problem Redundant examples in AL strategies waste annotation effort.
method A2 Learning actively adapts to deep learning models to eliminate redundant examples.
result A2 Learning reduces data requirements by 3-25% on NLP tasks.
BERTopic enhances stock market prediction by analyzing sentiment in topic models.
problem Improving stock price prediction accuracy using sentiment analysis.
method Employed BERTopic for sentiment analysis of stock market comments integrated with deep learning models.
result Enhanced model performance through topic sentiment integration.
Lottery ticket hypothesis found in NLP and RL, improving model efficiency.
problem Improving deep neural network initialization strategies.
method Evaluated winning ticket initializations in NLP and RL using various models and tasks.
result Winning ticket initializations significantly improve model performance, even at extreme pruning rates.
Compression method reduces word embedding size for NLP models.
problem Memory constraints in deploying deep learning models for NLP tasks.
method Low rank matrix factorization during training to compress word embeddings.
result 90% compression with minimal accuracy loss for sentence classification tasks.
X-Transformer improves deep transformer performance on extreme multi-label text classification.
problem Classifying text with a large number of labels from a sparse label space.
method Fine-tuning deep transformer models for extreme multi-label text classification.
result X-Transformer achieves new state-of-the-art results on XMC benchmark datasets.
Torch-Struct simplifies structured prediction for deep learning.
problem Difficulty in integrating structured prediction algorithms with deep learning frameworks.
method Develops a library (Torch-Struct) that integrates structured prediction with vectorized, auto-differentiation-based frameworks.
result Significant performance gains over fast baselines and cross-algorithm efficiency.
Deep models for financial transactions are vulnerable to adversarial attacks, especially when adding transaction tokens.
problem Vulnerability of deep models to adversarial attacks on financial transaction records.
method Examine adversarial attacks and defenses on transaction records data, considering black-box attacks and adding transaction tokens.
result A few generated transactions can fool a deep-learning model, highlighting the need for robustness improvements.
Improved NER in medical text with few examples.
problem Limited annotated examples for NER in medical texts.
method Layer-wise initialization, hyperparameter tuning, pre-training data, custom word embeddings, optimizing OOV words.
result F1 score improved from 69.3% to 78.87%.
Model agnostic feature importance for DNNs in NLP.
problem Characterizing DNNs as black boxes and explaining their decision-making process.
method Phrase-wise feature importance calculation for model agnostic DNNs.
result Robust and generalizable approach to feature importance calculation.
Study analyzes deep learning models for financial sentiment in earnings calls.
problem Leveraging NLP for sentiment analysis in financial transcripts.
method Comparative analysis of BERT, FinBERT, and ULMFiT models.
result Models' strengths and limitations in financial sentiment analysis.
Paper proposes IIQ for compressing embedding vectors.
problem Memory issues in representing large vocabularies.
method Isotropic iterative quantization (IIQ) for binary compression.
result More than 30x compression ratio with comparable performance.
Study improves cryptocurrency price prediction using unlabeled text data.
problem Predicting cryptocurrency returns from unlabelled text data.
method Introduced weak learning approach to finetune BERT on unlabeled text data.
result Finetuning pretrained NLP models with weak labels enhances forecast accuracy.
Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We ass…
BigBird improves transformer performance on NLP tasks with longer sequences.
problem Quadratic dependency on sequence length in transformer models.
method Sparse attention mechanism reducing quadratic dependency to linear.
result Significant improvement in performance on NLP tasks like QA and summarization.
Survey of deep learning for Hindi text classification.
problem Limited research on morphologically rich, low-resource Hindi text classification.
method Comparison of CNN, LSTM, Transformer, BERT, and LASER for Hindi text classification.
result Multilingual pre-trained sentence embeddings outperform traditional architectures for Hindi text classification.
ST-GAN predicts stock trends using financial news and data.
problem Predicting financial trends in stock markets.
method ST-GAN combines NLP and technical indicators using GAN technology.
result Significant improvement over existing models in stock price forecasting.
Anti-phishing aims to detect phishing content/documents in a pool of textual data. This is an important problem in cybersecurity that can help to guard users from fraudulent information. Natural language processing (NLP) offers a natural solution for this problem as it is capable of analyzing the textual content to per…
Optimal Word2Vec hyper-parameters improve NLP tasks.
problem Finding the best Word2Vec hyper-parameters for NLP tasks.
method Empirical evaluation of various hyper-parameter combinations on NLP tasks.
result The best hyper-parameters vary by task, and high analogy scores don't always correlate with performance.
Deep learning models predict financial market trends from social media leaders.
problem Predicting financial market trends using social media data.
method Deep learning models trained on NLP analysis of leaders' Twitter handles.
result Substantial improvement in financial market prediction accuracy.
New findings show BERT subnetworks can train independently and transfer to various tasks.
problem Finding smaller subnetworks that can train independently and transfer to other tasks.
method Examined pre-trained BERT models for subnetworks that can train independently and transfer to various downstream tasks.
result Found subnetworks at 40% to 90% sparsity that can train independently and transfer to various tasks.
DCoM uses deep neural networks to detect semantic data types from raw column values.
problem Detecting semantic data types from dirty and unseen data.
method DCoM employs multi-input NLP-based deep neural networks trained on 686,765 data columns.
result DCoM outperforms existing methods significantly on 78 different semantic data types.
A new challenge and model for multitask NLP.
problem Improving general NLP models across multiple tasks.
method Natural Language Decathlon challenge and Multitask Question Answering Network (MQAN).
result MQAN achieves state-of-the-art performance on multiple tasks.
QUACKIE creates a new benchmark for NLP interpretability.
problem Evaluating NLP interpretability methods is challenging due to biased ground truths.
method Formulated a custom classification task from question-answering datasets, generating unbiased ground truths.
result Demonstrated the effectiveness of current interpretability methods on the new benchmark.
This study uses NLP to detect financial risks from documents.
problem Detecting and predicting financial risks in documents.
method NLP model design, text preprocessing, feature extraction, machine learning.
result NLP model effectively identifies and predicts financial risks.
HUBERT combines BERT's structure with TPRs to improve NLP task transfer.
problem Improving transferability among NLP tasks.
method Combines BERT's bidirectional Transformer structure with TPRs to learn shared data structures.
result HUBERT outperforms BERT on GLUE and HANS datasets, showing better transferability.
TextCNN, the convolutional neural network for text, is a useful deep learning algorithm for sentence classification tasks such as sentiment analysis and question classification. However, neural networks have long been known as black boxes because interpreting them is a challenging task. Researchers have developed sever…
The paper uses geometry to assess how hard examples are for NLP models.
problem Challenges in NLP datasets and classifiers, especially with shallow features.
method Information geometry to quantify example difficulty, exploring BERT, CNN, and fasttext.
result Deep learning models are vulnerable to word substitutions in difficult examples.
RoBERTa outperforms other pre-trained models in NER tasks.
problem Improving Named Entity Recognition (NER) performance.
method Fine-tuning four pre-trained models (BERT, ERNIE, ERNIE2.0-tiny, RoBERTa) on NER task.
result RoBERTa achieved state-of-the-art results on MSRA-2006 dataset.
Improves deep learning models by blending gradients from training loss and auxiliary objective.
problem Minimizing a single training loss while encouraging desirable model properties.
method Solves a bilevel optimization problem by combining training loss gradients and orthogonal projections of auxiliary gradients.
result Bloop method leads to better performance than other gradient surgery methods without EMA.
TX-Ray analyzes and quantifies model knowledge transfer in NLP.
problem Insufficient methods for explaining and quantifying model knowledge transfer in NLP.
method Modified computer vision explainability principle to NLP, visualizing feature preference distributions.
result TX-Ray reveals how self-supervised models learn linguistic abstractions and improves generalization.
New measure corrects news bias in NLP stock return forecasting.
problem Improving stock return and volatility forecasting accuracy.
method Hype-Adjusted Probability Measure, sentiment score equation.
result Significantly improved forecast accuracy for U.S. semiconductor tickers.
This paper analyzes crypto white papers under MiCAR, highlighting NLP's role.
problem Regulatory changes in crypto white papers under MiCAR.
method Survey of existing NLP applications, analysis of MiCAR changes.
result NLP can assist in regulatory compliance and white paper analysis.
Paper distills word embeddings to reduce dimensionality without sacrificing accuracy.
problem Reducing neural model size for practical deployment.
method Teacher-student model-based embedding distillation with ensemble learning.
result Significant reduction in model size (80x faster and lighter) with minimal accuracy loss.
This paper improves NLP interpretability by using sentence segments instead of words.
problem Limitations of word-based sampling in explaining complex BERT models.
method Using sentence segments as elementary building blocks for NLP interpretability.
result Improved fidelity of the explainer on a benchmark classification task.
GluonCV and GluonNLP simplify deep learning for CV and NLP.
problem Lack of easy-to-use deep learning tools for CV and NLP.
method Developed modular APIs and pre-trained models for rapid prototyping.
result Facilitates rapid prototyping and reproducible research in CV and NLP.
The paper quantifies the regularity of attention operations.
problem Quantifying the regularity of attention operations.
method Proposes a new mathematical framework using measure theory and integral operators.
result Proves attention operation is Lipschitz continuous on compact domains and provides an estimate of its Lipschitz constant.
SusGen-GPT improves financial NLP and ESG report generation.
problem Lack of advanced NLP tools for finance and ESG domains.
method Developed SusGen-30K dataset and SusGen-GPT models.
result Achieved state-of-the-art performance in financial NLP tasks.
Paper uses NLP to cluster patient visits for diagnosis validation.
problem Validating if similar patients receive similar diagnoses.
method Representation of medical visits using word embeddings, clustering patients' visits.
result Stable and separated segments of visits positively validated against diagnoses.
Paper explores DNNs in modulation recognition with channel effects and adversarial attacks.
problem Automatic modulation recognition in wireless communications.
method Deep neural networks applied to channel models and adversarial perturbations.
result Impact of channel variations and adversarial attacks on DNN performance.