Search Results for author:
Found 29 papers, 7 papers with code
GECko+: a Grammatical and Discourse Error Correction Tool
GECko+ : a Grammatical and Discourse Error Correction Tool We introduce GECko+, a web-based writing assistance tool for English that corrects errors both at the sentence and at the discourse level.
Comparing representations of long clinical texts for the task of patient note-identification
In this paper, we address the challenge of patient-note identification, which involves accurately matching an anonymized clinical note to its corresponding patient, represented by a set of related notes.
Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems
In this paper, we present a bias and sustainability focused investigation of Automatic Speech Recognition (ASR) systems, namely Whisper and Massively Multilingual Speech (MMS), which have achieved state-of-the-art (SOTA) performances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Exploring ReAct Prompting for Task-Oriented Dialogue: Insights and Shortcomings
Large language models (LLMs) gained immense popularity due to their impressive capabilities in unstructured conversations.
KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning
Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources, and are nowadays covering an ever increasing array of domains.
REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning
With the introduction of (large) language models, there has been significant concern about the unintended bias such models may inherit from their training data.
Any four real numbers are on all fours with analogy
This work presents a formalization of analogy on numbers that relies on generalized means.
On the Calibration of Epistemic Uncertainty: Principles, Paradoxes and Conflictual Loss
The calibration of predictive distributions has been widely studied in deep learning, but the same cannot be said about the more specific epistemic uncertainty as produced by Deep Ensembles, Bayesian Deep Networks, or Evidential Deep Networks.
Uncertainty Management in the Construction of Knowledge Graphs: a Survey
That is why recent efforts focus on automatic approaches, which represents a challenging task since it requires handling the uncertainty of extracted knowledge throughout its integration into the KG.
The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese
In the field of spoken language understanding, systems like Whisper and Multilingual Massive Speech (MMS) have shown state-of-the-art performances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adapting the adapters for code-switching in multilingual ASR
Recently, large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition (ASR) to many low-resource languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning
Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop.
A statistical approach to detect sensitive features in a group fairness setting
However, such an approach is subjective and does not guarantee that these features are the only ones to be considered as sensitive nor that they entail unfair (disparate) outcomes.
Solving morphological analogies: from retrieval to generation
We propose a deep learning (DL) framework to address and tackle two key tasks in AR: analogy detection and solving.
Clarity: an improved gradient method for producing quality visual counterfactual explanations
Visual counterfactual explanations identify modifications to an image that would change the prediction of a classifier.
Survey on Fairness Notions and Related Tensions
Fairness requirements to be satisfied while learning models created several types of tensions among the different notions of fairness and other desirable properties such as privacy and classification accuracy.
Component twin-width as a parameter for BINARY-CSP and its semiring generalisations
We illustrate the advantages of this framework by instantiating our general algorithmic approach on several classes of problems (e. g., the $H$-coloring problem and its variants), and showing that it improves the best complexity upper bounds in the literature for several well-known problems.
Galois theory for analogical classifiers
Analogical proportions are 4-ary relations that read "A is to B as C is to D".
Tackling Morphological Analogies Using Deep Learning -- Extended Version
We demonstrate our model's competitive performance on analogy detection and resolution over multiple languages.
On the Transferability of Neural Models of Morphological Analogies
Analogical proportions are statements expressed in the form "A is to B as C is to D" and are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP).
A Neural Approach for Detecting Morphological Analogies
In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e. g., the axiomatic approach as well as that based on Kolmogorov complexity.
Reducing Unintended Bias of ML Models on Tabular and Textual Data
Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML.
A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression
We show that while a convolutional network can be trained to correctly estimate well calibrated aleatoric uncertainty, -- the uncertainty due to the presence of noise in the images -- it is unable to generate a trustworthy ellipticity distribution when exposed to previously unseen data (i. e. here, blended scenes).
A Bayesian Neural Network based on Dropout Regulation
Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks, and are used in many application domains such as astrophysics, autonomous driving... BNN assume a prior over the weights of a neural network instead of point estimates, enabling in this way the estimation of both aleatoric and epistemic uncertainty of the model prediction. Moreover, a particular type of BNN, namely MC Dropout, assumes a Bernoulli distribution on the weights by using Dropout. Several attempts to optimize the dropout rate exist, e. g. using a variational approach. In this paper, we present a new method called "Dropout Regulation" (DR), which consists of automatically adjusting the dropout rate during training using a controller as used in automation. DR allows for a precise estimation of the uncertainty which is comparable to the state-of-the-art while remaining simple to implement.
Making ML models fairer through explanations: the case of LimeOut
To illustrate, we will revisit the case of "LimeOut" that was proposed to tackle "process fairness", which measures a model's reliance on sensitive or discriminatory features.
Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study
Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking.
LimeOut: An Ensemble Approach To Improve Process Fairness
To achieve both, we draw inspiration from "dropout" techniques in neural based approaches, and propose a framework that relies on "feature drop-out" to tackle process fairness.
Knowledge-Based Matching of $n$-ary Tuples
In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar.
