Chris Develder — accepted AI-conference papers

2023

BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance

Karel D'Oosterlinck, François Remy, Johannes Deleu, Thomas Demeester, Chris Develder, Klim Zaporojets +4

EMNLP 2023long findings

Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical literature is paramount for public safety, but involves slow and costly manual labor. We set out to improve drug safety monitoring (pharmacovigilance, PV) through the use of Natural Language Processing (NLP). We introduce Bi…

Cited by 0 Source ↗code ↗Scholar ↗

2022

Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators

Manu Lahariya, Craig Innes, Chris Develder, Subramanian Ramamoorthy

IROS 2022poster

Soft actuators offer a safe, adaptable approach to tasks like gentle grasping and dexterous manipulation. Creating accurate models to control such systems however is challenging due to the complex physics of deformable materials. Accurate Finite Element Method (FEM) models incur prohibitive computat…

Cited by 8 Source ↗Scholar ↗

2022

Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals

Maarten De Raedt, Fréderic Godin, Chris Develder, Thomas Demeester

EMNLP 2022main

For text classification tasks, finetuned language models perform remarkably well. Yet, they tend to rely on spurious patterns in training data, thus limiting their performance on out-of-distribution (OOD) test data. Among recent models aiming to avoid this spurious pattern problem, adding extra coun…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2022

TempEL: Linking Dynamically Evolving and Newly Emerging Entities

Klim Zaporojets, Lucie-Aimée Kaffee, Johannes Deleu, Thomas Demeester, Chris Develder, Isabelle Augenstein

NeurIPS 2022accept

In our continuously evolving world, entities change over time and new, previously non-existing or unknown, entities appear. We study how this evolutionary scenario impacts the performance on a well established entity linking (EL) task. For that study, we introduce TempEL, an entity linking dataset t…

Cited by 19 Source ↗PDF ↗code ↗Scholar ↗

2022

Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution

Klim Zaporojets, Johannes Deleu, Yiwei Jiang, Thomas Demeester, Chris Develder

ACL 2022short

We consider the task of document-level entity linking (EL), where it is important to make consistent decisions for entity mentions over the full document jointly. We aim to leverage explicit “connections” among mentions within the document itself: we propose to join EL and coreference resolution (co…

Cited by 13 Source ↗PDF ↗code ↗Scholar ↗

2021

A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks

Amir Hadifar, Sofie Labat, Veronique Hoste, Chris Develder, Thomas Demeester

NAACL 2021long

In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-spe…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2021

A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders

Maarten De Raedt, Fréderic Godin, Pieter Buteneers, Chris Develder, Thomas Demeester

EMNLP 2021main

Powerful sentence encoders trained for multiple languages are on the rise. These systems are capable of embedding a wide range of linguistic properties into vector representations. While explicit probing tasks can be used to verify the presence of specific linguistic properties, it is unclear whethe…

Cited by 2 Source ↗PDF ↗Scholar ↗