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Found 31 papers, 9 papers with code
Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents
The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect.
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models
High-quality multilingual training data is essential for effectively pretraining large language models (LLMs).
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
Using the PROBot LLM-PROM application, patients will be asked tailored questions about their individual challenges, and can give more detailed feedback on the progress of their treatment.
Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems
Image-based diagnostic decision support systems (DDSS) utilizing deep learning have the potential to optimize clinical workflows.
Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness
We introduce "pointer-guided segment ordering" (SO), a novel pre-training technique aimed at enhancing the contextual understanding of paragraph-level text representations in large language models.
Model-agnostic Body Part Relevance Assessment for Pedestrian Detection
Model-agnostic explanation methods for deep learning models are flexible regarding usability and availability.
SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet
One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction.
Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic Rules
We introduce a novel data generation method for contradiction detection, which leverages the generative power of large language models as well as linguistic rules.
Controlled Randomness Improves the Performance of Transformer Models
During the pre-training step of natural language models, the main objective is to learn a general representation of the pre-training dataset, usually requiring large amounts of textual data to capture the complexity and diversity of natural language.
Tokenizer Choice For LLM Training: Negligible or Crucial?
The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.
Informed Named Entity Recognition Decoding for Generative Language Models
Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools.
Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models
Auditing financial documents is a very tedious and time-consuming process.
Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training
This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD).
Word Sense Disambiguation as a Game of Neurosymbolic Darts
The core of our methodology is a neurosymbolic sense embedding, in terms of a configuration of nested balls in n-dimensional space.
sustain.AI: a Recommender System to analyze Sustainability Reports
We present sustainAI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies' sustainability reports.
Towards Linguistically Informed Multi-Objective Pre-Training for Natural Language Inference
We introduce a linguistically enhanced combination of pre-training methods for transformers.
Towards automating Numerical Consistency Checks in Financial Reports
It combines a financial named entity and relation extraction module with a BERT-based filtering and text pair classification component to extract KPIs from unstructured sentences before linking them to synonymous occurrences in the balance sheet and profit & loss statement.
Improving Chest X-Ray Classification by RNN-based Patient Monitoring
Chest X-Ray imaging is one of the most common radiological tools for detection of various pathologies related to the chest area and lung function.
Zero-Shot Text Matching for Automated Auditing using Sentence Transformers
Natural language processing methods have several applications in automated auditing, including document or passage classification, information retrieval, and question answering.
A Linguistic Investigation of Machine Learning based Contradiction Detection Models: An Empirical Analysis and Future Perspectives
We analyze two Natural Language Inference data sets with respect to their linguistic features.
KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents and link them to their numerical values and other attributes.
Ranked #1 on
Joint Entity and Relation Extraction
on KPI-EDGAR
Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
To help answer this, we first introduce an open-source modular library, RL4LMs (Reinforcement Learning for Language Models), for optimizing language generators with RL.
Gradient Flows for L2 Support Vector Machine Training
We explore the merits of training of support vector machines for binary classification by means of solving systems of ordinary differential equations.
KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports
We present KPI-BERT, a system which employs novel methods of named entity recognition (NER) and relation extraction (RE) to extract and link key performance indicators (KPIs), e. g. "revenue" or "interest expenses", of companies from real-world German financial documents.
Towards Bundle Adjustment for Satellite Imaging via Quantum Machine Learning
Given is a set of images, where all images show views of the same area at different points in time and from different viewpoints.
DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers
Prior research in the area of Natural Language Processing (NLP) has shown that including the syntactic structure of a sentence using a dependency parse tree while training a representation learning model improves the performance on downstream tasks.
Generative Deep Learning Techniques for Password Generation
Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates.
NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing Tasks
Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks.
Adiabatic Quantum Computing for Binary Clustering
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest.
Rapid Prediction of Player Retention in Free-to-Play Mobile Games
Predicting and improving player retention is crucial to the success of mobile Free-to-Play games.
