no code implementations • FNP (COLING) 2020 • Maren Pielka, Rajkumar Ramamurthy, Anna Ladi, Eduardo Brito, Clayton Chapman, Paul Mayer, Rafet Sifa
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.
no code implementations • 27 Nov 2023 • Maurice Günder, Sneha Banerjee, Rafet Sifa, Christian Bauckhage
Model-agnostic explanation methods for deep learning models are flexible regarding usability and availability.
no code implementations • 6 Nov 2023 • Maurice Günder, Facundo Ramón Ispizua Yamati, Abel Andree Barreto Alcántara, Anne-Katrin Mahlein, Rafet Sifa, Christian Bauckhage
One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction.
1 code implementation • 23 Oct 2023 • Maren Pielka, Svetlana Schmidt, Rafet Sifa
We introduce a novel data generation method for contradiction detection, which leverages the generative power of large language models as well as linguistic rules.
no code implementations • 20 Oct 2023 • Tobias Deußer, Cong Zhao, Wolfgang Krämer, David Leonhard, Christian Bauckhage, Rafet Sifa
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.
no code implementations • 12 Oct 2023 • Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann, Max Lübbering, Johannes Leveling, Katrin Klug, Jan Ebert, Niclas Doll, Jasper Schulze Buschhoff, Charvi Jain, Alexander Arno Weber, Lena Jurkschat, Hammam Abdelwahab, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Samuel Weinbach, Rafet Sifa, Stefan Kesselheim, Nicolas Flores-Herr
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.
no code implementations • 15 Aug 2023 • Tobias Deußer, Lars Hillebrand, Christian Bauckhage, Rafet Sifa
Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools.
no code implementations • 11 Aug 2023 • Lars Hillebrand, Armin Berger, Tobias Deußer, Tim Dilmaghani, Mohamed Khaled, Bernd Kliem, Rüdiger Loitz, Maren Pielka, David Leonhard, Christian Bauckhage, Rafet Sifa
Auditing financial documents is a very tedious and time-consuming process.
1 code implementation • 27 Jul 2023 • Mohammad Majd Saad Al Deen, Maren Pielka, Jörn Hees, Bouthaina Soulef Abdou, Rafet Sifa
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).
no code implementations • 25 Jul 2023 • Tiansi Dong, Rafet Sifa
The core of our methodology is a neurosymbolic sense embedding, in terms of a configuration of nested balls in n-dimensional space.
1 code implementation • 15 May 2023 • Lars Hillebrand, Maren Pielka, David Leonhard, Tobias Deußer, Tim Dilmaghani, Bernd Kliem, Rüdiger Loitz, Milad Morad, Christian Temath, Thiago Bell, Robin Stenzel, Rafet Sifa
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.
no code implementations • 14 Dec 2022 • Maren Pielka, Svetlana Schmidt, Lisa Pucknat, Rafet Sifa
We introduce a linguistically enhanced combination of pre-training methods for transformers.
no code implementations • 11 Nov 2022 • Lars Hillebrand, Tobias Deußer, Tim Dilmaghani, Bernd Kliem, Rüdiger Loitz, Christian Bauckhage, Rafet Sifa
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.
no code implementations • 28 Oct 2022 • David Biesner, Maren Pielka, Rajkumar Ramamurthy, Tim Dilmaghani, Bernd Kliem, Rüdiger Loitz, Rafet Sifa
Natural language processing methods have several applications in automated auditing, including document or passage classification, information retrieval, and question answering.
no code implementations • 28 Oct 2022 • David Biesner, Helen Schneider, Benjamin Wulff, Ulrike Attenberger, Rafet Sifa
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.
no code implementations • 19 Oct 2022 • Maren Pielka, Felix Rode, Lisa Pucknat, Tobias Deußer, Rafet Sifa
We analyze two Natural Language Inference data sets with respect to their linguistic features.
1 code implementation • 17 Oct 2022 • Tobias Deußer, Syed Musharraf Ali, Lars Hillebrand, Desiana Nurchalifah, Basil Jacob, Christian Bauckhage, Rafet Sifa
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
3 code implementations • 3 Oct 2022 • Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, Yejin Choi
To help answer this, we first introduce an open-source modular library, RL4LMs (Reinforcement Learning for Language Models), for optimizing language generators with RL.
no code implementations • 8 Aug 2022 • Christian Bauckhage, Helen Schneider, Benjamin Wulff, Rafet Sifa
We explore the merits of training of support vector machines for binary classification by means of solving systems of ordinary differential equations.
no code implementations • 3 Aug 2022 • Lars Hillebrand, Tobias Deußer, Tim Dilmaghani, Bernd Kliem, Rüdiger Loitz, Christian Bauckhage, Rafet Sifa
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.
no code implementations • 23 Apr 2022 • Nico Piatkowski, Thore Gerlach, Romain Hugues, Rafet Sifa, Christian Bauckhage, Frederic Barbaresco
Given is a set of images, where all images show views of the same area at different points in time and from different viewpoints.
1 code implementation • IEEE SSCI 2021 • Abdul Wahab, Rafet Sifa
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.
no code implementations • 10 Dec 2020 • David Biesner, Kostadin Cvejoski, Bogdan Georgiev, Rafet Sifa, Erik Krupicka
Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates.
1 code implementation • 16 Nov 2020 • Rajkumar Ramamurthy, Rafet Sifa, Christian Bauckhage
Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks.
no code implementations • 17 Jun 2017 • Christian Bauckhage, Eduardo Brito, Kostadin Cvejoski, Cesar Ojeda, Rafet Sifa, Stefan Wrobel
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest.
no code implementations • 12 Jul 2016 • Anders Drachen, Eric Thurston Lundquist, Yungjen Kung, Pranav Simha Rao, Diego Klabjan, Rafet Sifa, Julian Runge
Predicting and improving player retention is crucial to the success of mobile Free-to-Play games.