Publications associées (23)

Kinesthetic motor-imagery training improves performance on lexical-semantic access

Sylvain Jean-François Harquel, Camille Bonnet

The objective of this study was to evaluate the effect of Motor Imagery (MI) training on language comprehension. In line with literature suggesting an intimate relationship between the language and the motor system, we proposed that a MI-training could imp ...
PUBLIC LIBRARY SCIENCE2022

Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling

Vinitra Swamy, Thiemo Wambsganss

Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in di ...
2022

Further results on latent discourse models and word embeddings

Youssef Allouah

We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic ...
MICROTOME PUBL2021

Multiple Hypothesis Semantic Mapping for Robust Data Association

Roland Siegwart

In this letter, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomou ...
2019

On Modeling the Synergy Between Acoustic and Lexical Information for Pronunciation Lexicon Development

Marzieh Razavi

State-of-the-art automatic speech recognition (ASR) and text-to-speech systems require a pronunciation lexicon that maps each word to a sequence of phones. Manual development of lexicons is costly as it needs linguistic knowledge and human expertise. To fa ...
EPFL2017

Functional BIP: Embedding connectors in functional programming languages

Romain Edelmann, Joseph Sifakis, Simon Bliudze

This paper presents a theoretical foundation for functional language implementations of Behaviour–Interaction–Priority (BIP). We introduce a set of connector combinators describing synchronisation, data transfer, priorities and dynamicity in a principled w ...
Elsevier Science Inc2017

Learning Explainable User Sentiment and Preferences for Information Filtering

Nikolaos Pappas

In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...
EPFL2016

Acoustic and Lexical Resource Constrained ASR using Language-Independent Acoustic Model and Language-Dependent Probabilistic Lexical Model

Ramya Rasipuram

One of the key challenges involved in building statistical automatic speech recognition (ASR) systems is modeling the relationship between subword units or “lexical units” and acoustic feature observations. To model this relationship two types of resources ...
2015

Acoustic and Lexical Resource Constrained ASR using Language-Independent Acoustic Model and Language-Dependent Probabilistic Lexical Model

Ramya Rasipuram

One of the key challenge involved in building a statistical automatic speech recognition (ASR) system is modeling the relationship between lexical units (that are based on subword units in the pronunciation lexicon) and acoustic feature observations. To mo ...
Idiap2014

Architecture Internalisation in BIP

Joseph Sifakis, Simon Bliudze

We consider two approaches for building component-based systems, which we call respectively architecture-based and architecture-agnostic. The former consists in describing coordination constraints in a purely declarative manner through parameterizable glue ...
ACM2014

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.