Search Results for author:
Found 49 papers, 31 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.
An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues
The ability to track fine-grained emotions in customer service dialogues has many real-world applications, but has not been studied extensively.
Variation in the Expression and Annotation of Emotions: A Wizard of Oz Pilot Study
Finally, for the third premise, we observed a positive correlation between the internal-external agreement on emotion labels and the personality traits conscientiousness and extraversion.
Lazy Low-Resource Coreference Resolution: a Study on Leveraging Black-Box Translation Tools
Large annotated corpora for coreference resolution are available for few languages.
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.
Neural Bayesian Network Understudy
Bayesian Networks may be appealing for clinical decision-making due to their inclusion of causal knowledge, but their practical adoption remains limited as a result of their inability to deal with unstructured data.
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.
BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)
This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts.
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.
Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines
Models for bankruptcy prediction are useful in several real-world scenarios, and multiple research contributions have been devoted to the task, based on structured (numerical) as well as unstructured (textual) data.
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.
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.
Overly Optimistic Prediction Results on Imbalanced Data: a Case Study of Flaws and Benefits when Applying Over-sampling
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth.
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.
System Identification with Time-Aware Neural Sequence Models
Established recurrent neural networks are well-suited to solve a wide variety of prediction tasks involving discrete sequences.
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
Neural Probabilistic Logic Programming in DeepProbLog
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.
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.
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations.
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
Jack the Reader -- A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
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.
Jack the Reader - A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
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.
DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.
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
Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes
Recurrent neural networks are nowadays successfully used in an abundance of applications, going from text, speech and image processing to recommender systems.
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.
Adversarial Sets for Regularising Neural Link Predictors
The training objective is defined as a minimax problem, where an adversary finds the most offending adversarial examples by maximising the inconsistency loss, and the model is trained by jointly minimising a supervised loss and the inconsistency loss on the adversarial examples.
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.
Representation learning for very short texts using weighted word embedding aggregation
Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc.
Lifted Rule Injection for Relation Embeddings
Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks.
Efficiency Evaluation of Character-level RNN Training Schedules
We present four training and prediction schedules from the same character-level recurrent neural network.
Learning Semantic Similarity for Very Short Texts
We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching.
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.
