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Concept

SemEval

Publications associées (10)

Lightweight Cross-Lingual Sentence Representation Learning

Martin Jaggi, Prakhar Gupta

Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Learning computationally efficient static word and sentence representations

Prakhar Gupta

Most of the Natural Language Processing (NLP) algorithms involve use of distributed vector representations of linguistic units (primarily words and sentences) also known as embeddings in one way or another. These embeddings come in two flavours namely, sta ...

EPFL2021

Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages

Enno Hermann

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should ...

2021

Trustworthy Face Recognition: Improving Generalization of Deep Face Presentation Attack Detection

Amir Mohammadi

The extremely high recognition accuracy achieved by modern, convolutional neural network (CNN) based face recognition (FR) systems has contributed significantly to the adoption of such systems in a variety of applications, from mundane activities like unlo ...

EPFL2020

On the Effect of Word Order on Cross-lingual Sentiment Analysis

Alejandro Ramírez Atrio

Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (RNNS) or convolutional networks (CNNS). This is a problem for ...

2019

Word Sense Consistency in Statistical and Neural Machine Translation

Xiao Pu

Different senses of source words must often be rendered by different words in the target language when performing machine translation (MT). Selecting the correct translation of polysemous words can be done based on the contexts of use. However, state-of-th ...

EPFL2018

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Martin Jaggi, Claudiu-Cristian Musat

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as ...

2018

Taxonomy Induction Using Hypernym Subsequences

Karl Aberer, Hamza Harkous, Rémi Philippe Lebret, Amit Gupta

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic fram ...

2017

Automated Taxonomy Induction and its Applications

Amit Gupta

Machine-readable semantic knowledge in the form of taxonomies (i.e., a collection of is-a edges) has proved to be beneficial in an array of NLP tasks including inference, textual entailment, question answering and information extraction. Such widespread ut ...

EPFL2017

Intégration de connaissances syntaxiques et sémantiques dans les représentations vectorielles de textes : [application au calcul de similarités sémantiques dans le cadre du modèle DSIR]

Romaric Besançon

The notion of similarity between texts is fundamental for many applications of Natural Language Processing. For example, this notion is particularly useful for the applications designed for the management of information in large textual databases, such as ...

EPFL2002