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.
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 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.
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.
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model. We show that there is shared structure between different NLP datasets that HUBERT, but not BERT, is able to learn and leverage. We validate …
New pruning method retains model expressiveness for NLP tasks.
problem Pruning large pretrained transformer models for real-world deployment.
method Mixture Gaussian Prior Pruning (MGPP) algorithm.
result MGPP outperforms existing pruning methods in high sparsity settings.
Hungarian text processing improved with efficient, accurate NLP pipelines.
problem Improving text processing for Hungarian language.
method Implemented in spaCy framework, focusing on efficiency and accuracy.
result Near state-of-the-art performance in all text preprocessing steps.
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.
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.
COCKATIEL explains neural net models on NLP tasks by identifying meaningful concepts.
problem Transformer models are complex and hard to interpret.
method COCKATIEL uses NMF and sensitivity analysis to identify and rank concepts used by the model.
result COCKATIEL provides accurate and meaningful explanations without affecting model performance.
While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-)supervised training. Thus, for TX-Ray, we modify the established computer vision explainability principle…
BERT outperforms traditional machine learning in text classification tasks.
problem Comparing BERT to traditional machine learning methods for text classification.
method Empirical testing of BERT against TF-IDF-based machine learning models in various scenarios.
result BERT demonstrates superior performance and independence from text features.
This paper presents several strategies that can improve neural network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM networks enhanced with two forms of regul…
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.
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.
Framework quantifies financial NLP robustness under regime shifts.
problem Semantic and causal drift in financial news narratives.
method Four metrics: FCAS, PCS, TSV, NLICS.
result Transformer models are more affected by semantic drift.
BERTino is a lightweight Italian DistilBERT model for NLP tasks.
problem High computational and memory demands of large language models.
method Developed a DistilBERT model tailored for Italian language.
result F1 scores comparable to BERTBASE with significant speed improvements.
Researchers develop a method to interpret GNNs by identifying unnecessary edges in NLP models.
problem Understanding which parts of graphs contribute to NLP model predictions.
method A post-hoc method using differentiable edge masking to identify and drop unnecessary edges.
result Large proportions of edges can be dropped without affecting model performance, providing insights into model predictions.
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.
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.
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.
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.
This paper predicts legal proceedings status using NLP and machine learning.
problem Classify Brazilian legal proceedings into archived, active, and suspended categories.
method Combined NLP techniques with machine learning to classify legal proceedings sequences.
result Achieved maximum accuracy of 93% and top average F1 Scores of 89% (macro) and 93% (weighted).
Paper develops a method for valid inference using language model predictions from verbal autopsy narratives.
problem Valid inference from verbal autopsy narratives for public health decision-making.
method Develops multiPPI++ method for valid inference using NLP techniques for COD prediction.
result Demonstrates the effectiveness of multiPPI++ in handling transportability issues and recovering ground truth estimates.
Cryptonite tests NLP models with cryptic crossword clues.
problem Ambiguity in language poses a challenge for NLP models.
method Cryptic crossword dataset based on cryptic clues.
result Fine-tuning T5-Large on Cryptonite achieves only 7.6% accuracy.
NLP techniques improve drug discovery by analyzing chemical and protein text.
problem Improving drug discovery through better analysis of chemical and protein text.
method Natural language processing techniques applied to biochemical entities.
result Enhanced prediction of molecular properties and design of novel molecules.
Study uses LLMs and NLP to analyze 14,342 algorithmic trading papers.
problem Analyzing the vast literature on algorithmic trading.
method Filtering and analyzing 136 million papers, comparing traditional and modern methods.
result Demonstrates the effectiveness of LLMs in refining and answering complex questions.
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 neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017). However, these gains rely on the availability of large amounts of annotated examples, without which state-of-the-art performance is rare…
The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin, 2019). Intriguingly, this phenomenon suggests that initial…
Improved language model for French clinical reports achieves state-of-the-art performance in medical NLP tasks.
problem Lack of specialized language models for French clinical reports.
method Adapted a general pre-trained language model (CamemBERT) to French clinical reports using a corpus of 21M reports.
result Pretrained and fine-tuned models improved F1-score by 3 percentage points on APMed task.
Improved NLP performance with fewer parameters and less data using conditional multi-task learning.
problem Challenges in transferring knowledge across different NLP tasks, including overfitting, forgetting, and negative transfer.
method Proposes a novel Transformer architecture with conditional attention and task-conditioned modules for efficient parameter sharing and mitigating forgetting.
result Achieves state-of-the-art performance on 26 NLP tasks with 66% less data and 50% fewer parameters compared to existing methods.
New algorithm avoids spurious sharpness minimization for NLP models.
problem SAM fails in NLP, leading to performance degradation.
method Developed Functional-SAM, which modifies logit statistics instead of function geometry.
result Functional-SAM and combined methods outperform AdamW and SAM in NLP tasks.
SCROLLS benchmarks long text NLP tasks, improving existing models.
problem Short NLP benchmarks ignore long texts; SCROLLS addresses this.
method Handpicked long-text datasets for summarization, QA, and inference tasks.
result Improvement potential on SCROLLS tasks, as indicated by initial baselines.
LLM Pro Finance Suite enhances financial NLP with instruction-tuned models.
problem Limited NLP capabilities for financial tasks in generalist models.
method Instruction-tuned large language models fine-tuned on financial data.
result Consistent improvement over state-of-the-art baselines in finance tasks.
We address the problem of graph classification based only on structural information. Inspired by natural language processing techniques (NLP), our model sequentially embeds information to estimate class membership probabilities. Besides, we experiment with NLP-like variational regularization techniques, making the mode…
A benchmark for NLP models trained on text datasets.
problem Limited access to high-performance clusters for NAS experiments.
method Created a search space for recurrent neural networks on text datasets and trained 14k architectures.
result Demonstrated the potential of precomputed NAS results for NLP.
Concerns about interpretability, computational resources, and principled inductive priors have motivated efforts to engineer sparse neural models for NLP tasks. If sparsity is important for NLP, might well-trained neural models naturally become roughly sparse? Using the Taxi-Euclidean norm to measure sparsity, we find …
The paper explores how word embeddings affect the stability of downstream NLP models.
problem Small changes in training data can cause significant changes in model predictions.
method Empirical and theoretical analysis of embedding instability, including the introduction of eigenspace instability measure.
result Increasing embedding memory can reduce the disagreement in predictions by 5% to 37%.
Study recommends PLM choices for minimizing calibration error in NLP tasks.
problem Minimizing calibration error in PLM-based NLP predictions.
method Compared various options for PLM encoding, size, uncertainty quantifier, and fine-tuning loss.
result Recommendations for a well-calibrated PLM-based prediction pipeline.
Since deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks, and in some cases, they are even outperforming humans. Such high performance can be explained by efficient knowledge representation of deep learnin…
Universal Language Model for Fine-tuning [arXiv:1801.06146] (ULMFiT) is one of the first NLP methods for efficient inductive transfer learning. Unsupervised pretraining results in improvements on many NLP tasks for English. In this paper, we describe a new method that uses subword tokenization to adapt ULMFiT to langua…
Study uses NLP to analyze emotions and challenges of young people with IDD.
problem Challenges faced by young people with IDD during transition to adulthood.
method Natural language processing, unsupervised machine learning, topic modeling.
result NLP methods can assist psychologists in analyzing emotions and summarizing key topics.
FairLangProc simplifies fairness in NLP models for Python users.
problem Addressing bias in NLP models for decision-making contexts.
method Develops a Python package for implementing fairness metrics and algorithms.
result Promotes the use of bias mitigation techniques in NLP.
HuSpaCy offers an industrial-grade Hungarian NLP toolkit.
problem Lack of suitable open-source Hungarian NLP pipelines.
method Built on spaCy, HuSpaCy includes lemmatization, morphosyntactic analysis, entity recognition, and word embeddings.
result HuSpaCy achieves high accuracy with resource-efficient prediction.
The Hype Index measures media attention to equities using NLP.
problem Quantifying media attention to equities for volatility analysis.
method Constructs News Count-Based and Capitalization Adjusted Hype Indices using NLP.
result The Hype Index family provides valuable tools for stock volatility analysis.
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, …
This work explains why GANs are less used for NLP tasks.
problem Why adversarial approaches like GANs are not widely used for NLP tasks.
method Theoretical analysis and reductions showing that maximizing likelihood is equivalent to minimizing distinguishability for certain models.
result Maximizing likelihood is as effective as minimizing distinguishability for NLP tasks.