Search Results for author:
Found 55 papers, 27 papers with code
UGent-T2K at the 2nd DialDoc Shared Task: A Retrieval-Focused Dialog System Grounded in Multiple Documents
Further, error analysis reveals two major failure cases, to be addressed in future work: (i) in case of topic shift within the dialog, retrieval often fails to select the correct grounding document(s), and (ii) generation sometimes fails to use the correctly retrieved grounding passage.
Lazy Low-Resource Coreference Resolution: a Study on Leveraging Black-Box Translation Tools
Large annotated corpora for coreference resolution are available for few languages.
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers
Accounting for about 25% of final energy consumption globally, the residential sector is an important (potential) source of energy flexibility.
Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees
We aim to address this challenging problem and introduce a reinforcement learning-based approach using differentiable decision trees.
In-Context Learning for Extreme Multi-Label Classification
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to demonstrate every class in a prompt.
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism
Our proposed control framework takes a risk-sensitive perspective, allowing BRPs to adjust their risk preferences: we aim to optimize a weighted sum of the arbitrage profit and a risk measure while constraining the daily number of cycles for the battery.
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks
Thus, we study MCTS specifically for building demand response.
Flexible Model Interpretability through Natural Language Model Editing
Model interpretability and model editing are crucial goals in the age of large language models.
Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study
The brittleness of finetuned language model performance on out-of-distribution (OOD) test samples in unseen domains has been well-studied for English, yet is unexplored for multi-lingual models.
Cross-Lingual Sentiment Classification
Sentiment Analysis
+3
Personality Style Recognition via Machine Learning: Identifying Anaclitic and Introjective Personality Styles from Patients' Speech
In disentangling the heterogeneity observed in psychopathology, personality of the patients is considered crucial.
Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households
Given its substantial contribution of 40\% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid.
Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System
Specifically, we train an advanced forecasting model (a temporal fusion transformer) using data from multiple different households, and then finetune this global model on a new household with limited data (i. e. only a few days).
Career Path Prediction using Resume Representation Learning and Skill-based Matching
The impact of person-job fit on job satisfaction and performance is widely acknowledged, which highlights the importance of providing workers with next steps at the right time in their career.
CAW-coref: Conjunction-Aware Word-level Coreference Resolution
State-of-the-art coreference resolutions systems depend on multiple LLM calls per document and are thus prohibitively expensive for many use cases (e. g., information extraction with large corpora).
Ranked #3 on
Coreference Resolution
on OntoNotes
Distractor generation for multiple-choice questions with predictive prompting and large language models
We also show the gains of our approach 1 in generating high-quality distractors by comparing it with a zero-shot ChatGPT and a few-shot ChatGPT prompted with static examples.
Extreme Multi-Label Skill Extraction Training using Large Language Models
Online job ads serve as a valuable source of information for skill requirements, playing a crucial role in labor market analysis and e-recruitment processes.
Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning
Since performing exercises (including, e. g., practice tests) forms a crucial component of learning, and creating such exercises requires non-trivial effort from the teacher, there is a great value in automatic exercise generation in digital tools in education.
IDAS: Intent Discovery with Abstractive Summarization
Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents.
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance
We introduce BioDEX, a large-scale resource for Biomedical adverse Drug Event Extraction, rooted in the historical output of drug safety reporting in the U. S. BioDEX consists of 65k abstracts and 19k full-text biomedical papers with 256k associated document-level safety reports created by medical experts.
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
For that study, we introduce TempEL, an entity linking dataset that consists of time-stratified English Wikipedia snapshots from 2013 to 2022, from which we collect both anchor mentions of entities, and these target entities' descriptions.
PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control
As a physics-informed reinforcement learning framework for building control, PhysQ forms a step in bridging the gap between conventional model-based control and data-intensive control based on reinforcement learning.
Learning to Reuse Distractors to support Multiple Choice Question Generation in Education
This paper studies how a large existing set of manually created answers and distractors for questions over a variety of domains, subjects, and languages can be leveraged to help teachers in creating new MCQs, by the smart reuse of existing distractors.
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals
We demonstrate the effectiveness of our approach in sentiment classification, using IMDb data for training and other sets for OOD tests (i. e., Amazon, SemEval and Yelp).
EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain
Thus, our versatile dataset can be used for both question and distractor generation, as well as to explore new challenges such as question format conversion.
Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction
We introduce a manually annotated evaluation benchmark for skill extraction based on the ESCO taxonomy, on which we validate our models.
CookDial: A dataset for task-oriented dialogs grounded in procedural documents
This work presents a new dialog dataset, CookDial, that facilitates research on task-oriented dialog systems with procedural knowledge understanding.
Physics Informed LSTM Network for Flexibility Identification in Evaporative Cooling Systems
These physics informed networks approximate the time-dependent relationship between control input and system response while enforcing the dynamics of the process in the neural network architecture.
Computationally efficient joint coordination of multiple electric vehicle charging points using reinforcement learning
A major challenge in todays power grid is to manage the increasing load from electric vehicle (EV) charging.
Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning
However, we note that the computationally expensive cost function adopted in the previous research leads to large training times, which limits the feasibility and practicality of the approach.
Defining a synthetic data generator for realistic electric vehicle charging sessions
Addressing this need for publicly available and realistic data, we develop a synthetic data generator (SDG) for EV charging sessions.
Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators
We simulate the task of using DEA to pull a coin along a surface with frictional contact, using FEM, and evaluate the physics-informed model for simulation, control, and inference.
Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings
To combine the interpretability of white/gray box physics models and the expressive power of neural networks, we propose a physics informed neural network approach for this modeling task.
JobBERT: Understanding Job Titles through Skills
Job titles form a cornerstone of today's human resources (HR) processes.
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution
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.
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution
The used KB entity representations are learned from either (i) hyperlinked text documents (Wikipedia), or (ii) a knowledge graph (Wikidata), and appear complementary in raising IE performance.
Ranked #1 on
Relation Extraction
on DWIE
A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks
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.
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders
Powerful sentence encoders trained for multiple languages are on the rise.
Recipe Instruction Semantics Corpus (RISeC): Resolving Semantic Structure and Zero Anaphora in Recipes
We propose a newly annotated dataset for information extraction on recipes.
DWIE: an entity-centric dataset for multi-task document-level information extraction
Second, the document-level multi-task annotations require the models to transfer information between entity mentions located in different parts of the document, as well as between different tasks, in a joint learning setting.
Ranked #1 on
Coreference Resolution
on DWIE
(Avg. F1 metric)
Solving Arithmetic Word Problems by Scoring Equations with Recursive Neural Networks
Recent works use automatic extraction and ranking of candidate solution equations providing the answer to arithmetic word problems.
Block-wise Dynamic Sparseness
In this paper, we present a new method for \emph{dynamic sparseness}, whereby part of the computations are omitted dynamically, based on the input.
A Self-Training Approach for Short Text Clustering
Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations of the short texts.
Ranked #2 on
Short Text Clustering
on Searchsnippets
Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A
This paper describes IDLab{'}s text classification systems submitted to Task A as part of the CLPsych 2019 shared task.
Sub-event detection from Twitter streams as a sequence labeling problem
This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also directly on the stream level.
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
In this paper, we propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of EV charging stations.
Predefined Sparseness in Recurrent Sequence Models
Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models.
Adversarial training for multi-context joint entity and relation extraction
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data.
Ranked #7 on
Relation Extraction
on ACE 2004
Prior Attention for Style-aware Sequence-to-Sequence Models
We extend sequence-to-sequence models with the possibility to control the characteristics or style of the generated output, via attention that is generated a priori (before decoding) from a latent code vector.
Predicting Psychological Health from Childhood Essays. The UGent-IDLab CLPsych 2018 Shared Task System.
This paper describes the IDLab system submitted to Task A of the CLPsych 2018 shared task.
Joint entity recognition and relation extraction as a multi-head selection problem
State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers.
Ranked #7 on
Relation Extraction
on CoNLL04
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation that would arise from the subtasks one after the other in a pipelined fashion, and (ii) exploiting the interactions between the subtasks.
Break it Down for Me: A Study in Automated Lyric Annotation
Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike.
Reconstructing the house from the ad: Structured prediction on real estate classifieds
In this paper, we address the (to the best of our knowledge) new problem of extracting a structured description of real estate properties from their natural language descriptions in classifieds.
Knowledge Base Population using Semantic Label Propagation
We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision.
