6 citations · 34 across the 18 of their papers we have counts for
20 papers
Large Language Models Enable Few-Shot Clustering
Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence +2
Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the u…
Uncertainty Propagation in Node Classification
Zhao Xu, Carolin Lawrence, Ammar Shaker +1
Quantifying predictive uncertainty of neural networks has recently attracted increasing attention. In this work, we focus on measuring uncertainty of graph neural networks (GNNs) f…
State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions
Cheng Wang, Carolin Lawrence, Mathias Niepert
Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, they are often treated as black-box models and as such it is…
Multi-Source Survival Domain Adaptation
Ammar Shaker, Carolin Lawrence
Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the…
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim +5
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) task…
Human-Centric Research for NLP: Towards a Definition and Guiding Questions
Bhushan Kotnis, Kiril Gashteovski, Julia Gastinger +7
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes rese…
A Human-Centric Assessment Framework for AI
Sascha Saralajew, Ammar Shaker, Zhao Xu +5
With the rise of AI systems in real-world applications comes the need for reliable and trustworthy AI. An essential aspect of this are explainable AI systems. However, there is no…
milIE: Modular & Iterative Multilingual Open Information Extraction
Bhushan Kotnis, Kiril Gashteovski, Daniel Oñoro Rubio +5
Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots…
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation
Kiril Gashteovski, Mingying Yu, Bhushan Kotnis +3
Intrinsic evaluations of OIE systems are carried out either manually -- with human evaluators judging the correctness of extractions -- or automatically, on standardized benchmarks…
VEGN: Variant Effect Prediction with Graph Neural Networks
Jun Cheng, Carolin Lawrence, Mathias Niepert
Genetic mutations can cause disease by disrupting normal gene function. Identifying the disease-causing mutations from millions of genetic variants within an individual patient is…
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs
Cheng Wang, Carolin Lawrence, Mathias Niepert
Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stoch…
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks
Julia Kreutzer, Stefan Riezler, Carolin Lawrence
Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction log…
Explaining Neural Matrix Factorization with Gradient Rollback
Carolin Lawrence, Timo Sztyler, Mathias Niepert
Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the infl…
Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders
Bhushan Kotnis, Carolin Lawrence, Mathias Niepert
Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries. In this work we address the more ambitious challenge of p…
Attending to Future Tokens For Bidirectional Sequence Generation
Carolin Lawrence, Bhushan Kotnis, Mathias Niepert
Neural sequence generation is typically performed token-by-token and left-to-right. Whenever a token is generated only previously produced tokens are taken into consideration. In c…
Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss
Laura Jehl, Carolin Lawrence, Stefan Riezler
In many machine learning scenarios, supervision by gold labels is not available and consequently neural models cannot be trained directly by maximum likelihood estimation (MLE). In…
Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing
Carolin Lawrence, Stefan Riezler
In semantic parsing for question-answering, it is often too expensive to collect gold parses or even gold answers as supervision signals. We propose to convert model outputs into a…
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback
Carolin Lawrence, Stefan Riezler
Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target sy…
Counterfactual Learning for Machine Translation: Degeneracies and Solutions
Carolin Lawrence, Pratik Gajane, Stefan Riezler
Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions. In order to avoid…
Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation
Carolin Lawrence, Artem Sokolov, Stefan Riezler
The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that…