Patrick Hohenecker


Controlling Text Edition by Changing Answers of Specific Questions
Lei Sha | Patrick Hohenecker | Thomas Lukasiewicz
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction
Patrick Hohenecker | Frank Mtumbuka | Vid Kocijan | Thomas Lukasiewicz
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form <subject,predicate, object>. For example, given the sentence “Beethoven composed the Ode to Joy.”, we are expected to extract the triple <Beethoven, composed, Ode to Joy>. In this work, we systematically compare different neural network architectures and training approaches, and improve the performance of the currently best models on the OIE16 benchmark (Stanovsky and Dagan, 2016) by 0.421 F1 score and 0.420 AUC-PR, respectively, in our experiments (i.e., by more than 200% in both cases). Furthermore, we show that appropriate problem and loss formulations often affect the performance more than the network architecture.