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Found 33 papers, 20 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.
Clinical Reasoning over Tabular Data and Text with Bayesian Networks
Bayesian networks are well-suited for clinical reasoning on tabular data, but are less compatible with natural language data, for which neural networks provide a successful framework.
Exploring the Temperature-Dependent Phase Transition in Modern Hopfield Networks
To achieve this, the distribution of energy minima is tracked in a simplified MHN in which equidistant normalised patterns are stored.
Accelerating Hierarchical Associative Memory: A Deep Equilibrium Approach
Hierarchical Associative Memory models have recently been proposed as a versatile extension of continuous Hopfield networks.
Training a Hopfield Variational Autoencoder with Equilibrium Propagation
In spite of its theoretical guarantees, its application in the AI domain remains limited to the discriminative setting.
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.
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.
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.
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.
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.
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
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.
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.
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.
