Search Results for author: Saturnino Luz

Found 14 papers, 3 papers with code

Exploring Neural Language Models via Analysis of Local and Global Self-Attention Spaces

1 code implementation EACL (Hackashop) 2021 Blaž Škrlj, Shane Sheehan, Nika Eržen, Marko Robnik-Šikonja, Saturnino Luz, Senja Pollak

Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text completion and machine translation.

Machine Translation Pretrained Language Models +5

TeMoTopic: Temporal Mosaic Visualisation of Topic Distribution, Keywords, and Context

no code implementations EACL (Hackashop) 2021 Shane Sheehan, Saturnino Luz, Masood Masoodian

In this paper we present TeMoTopic, a visualization component for temporal exploration of topics in text corpora.

Computational linguistics and Natural Language Processing

no code implementations14 Jun 2022 Saturnino Luz

This chapter provides an introduction to computational linguistics methods, with focus on their applications to the practice and study of translation.


Detecting cognitive decline using speech only: The ADReSSo Challenge

no code implementations23 Mar 2021 Saturnino Luz, Fasih Haider, Sofia de la Fuente, Davida Fromm, Brian MacWhinney

Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline.

General Classification regression

Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review

no code implementations12 Oct 2020 Sofia de la Fuente Garcia, Craig Ritchie, Saturnino Luz

We concluded that the main limitations of the field are poor standardisation, limited comparability of results, and a degree of disconnect between study aims and clinical applications.

AttViz: Online exploration of self-attention for transparent neural language modeling

1 code implementation12 May 2020 Blaž Škrlj, Nika Eržen, Shane Sheehan, Saturnino Luz, Marko Robnik-Šikonja, Senja Pollak

Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation.

Language Modelling text-classification +2

Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge

no code implementations14 Apr 2020 Saturnino Luz, Fasih Haider, Sofia de la Fuente, Davida Fromm, Brian MacWhinney

ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task.

Classification General Classification +1

Potential Applications of Machine Learning at Multidisciplinary Medical Team Meetings

no code implementations3 Nov 2019 Bridget Kane, Jing Su, Saturnino Luz

While machine learning (ML) systems have produced great advances in several domains, their use in support of complex cooperative work remains a research challenge.

BIG-bench Machine Learning

Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods

no code implementations28 Aug 2019 Fasih Haider, Senja Pollak, Pierre Albert, Saturnino Luz

A machine learning model trained on a smaller feature set will reduce the memory and computational resources of an emotion recognition system which can result in lowering the barriers for use of health monitoring technology.

Emotion Recognition

A Method for Analysis of Patient Speech in Dialogue for Dementia Detection

1 code implementation25 Nov 2018 Saturnino Luz, Sofia de la Fuente, Pierre Albert

We present an approach to automatic detection of Alzheimer's type dementia based on characteristics of spontaneous spoken language dialogue consisting of interviews recorded in natural settings.

The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus

no code implementations LREC 2016 Akira Hayakawa, Saturnino Luz, Loredana Cerrato, Nick Campbell

The corpus design is inspired by the HCRC Map Task Corpus which was initially designed to support the investigation of linguistic phenomena, and has been the focus of a variety of studies of communicative behaviour.

Automatic Speech Recognition Machine Translation +2

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