Search Results for author:
Found 38 papers, 20 papers with code
AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization
Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization).
Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data
Ecological Momentary Assessment (EMA) data is organized in multiple levels (per-subject, per-day, etc.)
Accumulated Gradient Normalization
This work addresses the instability in asynchronous data parallel optimization.
Massive Open Online Courses Temporal Profiling for Dropout Prediction
Massive Open Online Courses (MOOCs) are attracting the attention of people all over the world.
A retrieval-based dialogue system utilizing utterance and context embeddings
Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations.
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings.
LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color
Yet, conditional generative adversarial networks can be used to generate logos that could help designers in their creative process.
Exploring the context of recurrent neural network based conversational agents
Conversational agents have begun to rise both in the academic (in terms of research) and commercial (in terms of applications) world.
Towards Controlled Transformation of Sentiment in Sentences
The autoencoder is tested on how well it is able to change the sentiment of an encoded phrase and it was found that such a task is possible.
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction
Time Series forecasting (univariate and multivariate) is a problem of high complexity due the different patterns that have to be detected in the input, ranging from high to low frequencies ones.
#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
Inspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended).
LoGANv2: Conditional Style-Based Logo Generation with Generative Adversarial Networks
Domains such as logo synthesis, in which the data has a high degree of multi-modality, still pose a challenge for generative adversarial networks (GANs).
Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification
The tiled convolutional neural network (tiled CNN) has been applied only to computer vision for learning invariances.
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
On How2 English-Portuguese speech translation, we reduce latency to 0. 7 second (-84% rel.)
Sequence-To-Sequence Speech Recognition
speech-recognition
+1
Adapting End-to-End Speech Recognition for Readable Subtitles
The experiments show that with limited data far less than needed for training a model from scratch, we can adapt a Transformer-based ASR model to incorporate both transcription and compression capabilities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
"Thy algorithm shalt not bear false witness": An Evaluation of Multiclass Debiasing Methods on Word Embeddings
Specifically, in the natural language processing domain, it has been shown that social biases persist in word embeddings and are thus in danger of amplifying these biases when used.
Evaluating Bias In Dutch Word Embeddings
Recent research in Natural Language Processing has revealed that word embeddings can encode social biases present in the training data which can affect minorities in real world applications.
Can we detect harmony in artistic compositions? A machine learning approach
Harmony in visual compositions is a concept that cannot be defined or easily expressed mathematically, even by humans.
Analysing The Impact Of Linguistic Features On Cross-Lingual Transfer
As a result, one should not expect that for a target language $L_1$ there is a single language $L_2$ that is the best choice for any NLP task (for instance, for Bulgarian, the best source language is French on POS tagging, Russian on NER and Thai on NLI).
A Statutory Article Retrieval Dataset in French
Statutory article retrieval is the task of automatically retrieving law articles relevant to a legal question.
Ranked #1 on
Information Retrieval
on BSARD
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data
Interestingly, it is observed that in one of the two real-world datasets, knowledge distillation method achieves improved AUC scores (mean relative change of +17\% compared to personalized) showing how it can benefit EMA data classification and performance.
Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning
Inspired by the curvature of space-time (Einstein, 1921), we introduce Curved Contrastive Learning (CCL), a novel representation learning technique for learning the relative turn distance between utterance pairs in multi-turn dialogues.
Clustering individuals based on multivariate EMA time-series data
In the field of psychopathology, Ecological Momentary Assessment (EMA) methodological advancements have offered new opportunities to collect time-intensive, repeated and intra-individual measurements.
Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks
Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing.
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets
The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets.
Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media
To improve annotation accuracy and, thus, the detection of sponsored content, we propose using chatGPT to augment the annotation process with phrases identified as relevant features and brief explanations.
Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models
To support this approach, we introduce and release the Long-form Legal Question Answering (LLeQA) dataset, comprising 1, 868 expert-annotated legal questions in the French language, complete with detailed answers rooted in pertinent legal provisions.
IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements
Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights.
Larth: Dataset and Machine Translation for Etruscan
Etruscan is an ancient language spoken in Italy from the 7th century BC to the 1st century AD.
Regulation and NLP (RegNLP): Taming Large Language Models
Regulation studies are a rich source of knowledge on how to systematically deal with risk and uncertainty, as well as with scientific evidence, to evaluate and compare regulatory options.
Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance
However, it is believed that additional information of similar individuals is likely to enhance these models leading to better individuals' description.
Sequence Shortening for Context-Aware Machine Translation
In this study, we show that a special case of multi-encoder architecture, where the latent representation of the source sentence is cached and reused as the context in the next step, achieves higher accuracy on the contrastive datasets (where the models have to rank the correct translation among the provided sentences) and comparable BLEU and COMET scores as the single- and multi-encoder approaches.
Triple-Encoders: Representations That Fire Together, Wire Together
Search-based dialog models typically re-encode the dialog history at every turn, incurring high cost.
ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval
In this work, we present a novel modular dense retrieval model that learns from the rich data of a single high-resource language and effectively zero-shot transfers to a wide array of languages, thereby eliminating the need for language-specific labeled data.
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting
In the evolving field of psychopathology, the accurate assessment and forecasting of data derived from Ecological Momentary Assessment (EMA) is crucial.
A Cancel Culture Corpus through the Lens of Natural Language Processing
Cancel Culture as an Internet phenomenon has been previously explored from a social and legal science perspective.
Cultural Vocal Bursts Intensity Prediction
Sentiment Analysis
SeqAttack: On Adversarial Attacks for Named Entity Recognition
Named Entity Recognition is a fundamental task in information extraction and is an essential element for various Natural Language Processing pipelines.
