activity
20172023
most citedeSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing

37 citations · 277 across the 41 of their papers we have counts for

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Showing 2020Show all

11 papers · 1 filter

cs.CL2020

On Knowledge Distillation for Direct Speech Translation

Marco Gaido, Mattia A. Di Gangi, Matteo Negri +1

Direct speech translation (ST) has shown to be a complex task requiring knowledge transfer from its sub-tasks: automatic speech recognition (ASR) and machine translation (MT). For…

cs.CL2020

Breeding Gender-aware Direct Speech Translation Systems

Marco Gaido, Beatrice Savoldi, Luisa Bentivogli +2

In automatic speech translation (ST), traditional cascade approaches involving separate transcription and translation steps are giving ground to increasingly competitive and more r…

cs.LG2020★ 10 cited

Wide flat minima and optimal generalization in classifying high-dimensional Gaussian mixtures

Carlo Baldassi, Enrico M. Malatesta, Matteo Negri +1

We analyze the connection between minimizers with good generalizing properties and high local entropy regions of a threshold-linear classifier in Gaussian mixtures with the mean sq…

cs.CL2020★ 2 cited

On Target Segmentation for Direct Speech Translation

Mattia Antonino Di Gangi, Marco Gaido, Matteo Negri +1

Recent studies on direct speech translation show continuous improvements by means of data augmentation techniques and bigger deep learning models. While these methods are helping t…

cs.CL2020

Contextualized Translation of Automatically Segmented Speech

Marco Gaido, Mattia Antonino Di Gangi, Matteo Negri +2

Direct speech-to-text translation (ST) models are usually trained on corpora segmented at sentence level, but at inference time they are commonly fed with audio split by a voice ac…

cs.CL2020★ 4 cited

Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus

Luisa Bentivogli, Beatrice Savoldi, Matteo Negri +3

Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to t…

cs.CL2020

End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020

Marco Gaido, Mattia Antonino Di Gangi, Matteo Negri +1

This paper describes FBK's participation in the IWSLT 2020 offline speech translation (ST) task. The task evaluates systems' ability to translate English TED talks audio into Germa…

cs.LG2020★ 28 cited

Entropic gradient descent algorithms and wide flat minima

Fabrizio Pittorino, Carlo Lucibello, Christoph Feinauer +4

The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization ca…

cs.CL2020

Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?

Alina Karakanta, Matteo Negri, Marco Turchi

Subtitling is becoming increasingly important for disseminating information, given the enormous amounts of audiovisual content becoming available daily. Although Neural Machine Tra…

cs.CL2020★ 20 cited

Low Resource Neural Machine Translation: A Benchmark for Five African Languages

Surafel M. Lakew, Matteo Negri, Marco Turchi

Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five…

cs.CL2020

MuST-Cinema: a Speech-to-Subtitles corpus

Alina Karakanta, Matteo Negri, Marco Turchi

Growing needs in localising audiovisual content in multiple languages through subtitles call for the development of automatic solutions for human subtitling. Neural Machine Transla…