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

collaborators

50 papers

cs.CL2023★ 2 cited

Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023

Sara Papi, Marco Gaido, Matteo Negri

This paper describes the FBK's participation in the Simultaneous Translation and Automatic Subtitling tracks of the IWSLT 2023 Evaluation Campaign. Our submission focused on the us…

cs.CL2023★ 3 cited

AlignAtt: Using Attention-based Audio-Translation Alignments as a Guide for Simultaneous Speech Translation

Sara Papi, Marco Turchi, Matteo Negri

Attention is the core mechanism of today's most used architectures for natural language processing and has been analyzed from many perspectives, including its effectiveness for mac…

cs.CL2023★ 3 cited

When Good and Reproducible Results are a Giant with Feet of Clay: The Importance of Software Quality in NLP

Sara Papi, Marco Gaido, Andrea Pilzer +1

Despite its crucial role in research experiments, code correctness is often presumed only on the basis of the perceived quality of results. This assumption comes with the risk of e…

cs.CL2023★ 11 cited

Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges

Andrea Piergentili, Dennis Fucci, Beatrice Savoldi +2

Gender inclusivity in language technologies has become a prominent research topic. In this study, we explore gender-neutral translation (GNT) as a form of gender inclusivity and a…

cs.CL2022★ 7 cited

Attention as a Guide for Simultaneous Speech Translation

Sara Papi, Matteo Negri, Marco Turchi

The study of the attention mechanism has sparked interest in many fields, such as language modeling and machine translation. Although its patterns have been exploited to perform di…

cs.CL2022★ 5 cited

Joint Speech Translation and Named Entity Recognition

Marco Gaido, Sara Papi, Matteo Negri +1

Modern automatic translation systems aim at place the human at the center by providing contextual support and knowledge. In this context, a critical task is enriching the output wi…

cs.CL2022

Direct Speech Translation for Automatic Subtitling

Sara Papi, Marco Gaido, Alina Karakanta +3

Automatic subtitling is the task of automatically translating the speech of audiovisual content into short pieces of timed text, i.e. subtitles and their corresponding timestamps.…

cs.CL2022★ 2 cited

Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corpora

Sara Papi, Alina Karakanta, Matteo Negri +1

Speech translation for subtitling (SubST) is the task of automatically translating speech data into well-formed subtitles by inserting subtitle breaks compliant to specific display…

cs.CL2022★ 18 cited

Over-Generation Cannot Be Rewarded: Length-Adaptive Average Lagging for Simultaneous Speech Translation

Sara Papi, Marco Gaido, Matteo Negri +1

Simultaneous speech translation (SimulST) systems aim at generating their output with the lowest possible latency, which is normally computed in terms of Average Lagging (AL). In t…

cs.CL2022

Who Are We Talking About? Handling Person Names in Speech Translation

Marco Gaido, Matteo Negri, Marco Turchi

Recent work has shown that systems for speech translation (ST) -- similarly to automatic speech recognition (ASR) -- poorly handle person names. This shortcoming does not only lead…

cs.CL2022★ 16 cited

Efficient yet Competitive Speech Translation: FBK@IWSLT2022

Marco Gaido, Sara Papi, Dennis Fucci +3

The primary goal of this FBK's systems submission to the IWSLT 2022 offline and simultaneous speech translation tasks is to reduce model training costs without sacrificing translat…

cs.CL2022★ 9 cited

Does Simultaneous Speech Translation need Simultaneous Models?

Sara Papi, Marco Gaido, Matteo Negri +1

In simultaneous speech translation (SimulST), finding the best trade-off between high translation quality and low latency is a challenging task. To meet the latency constraints pos…

cs.CL2022★ 3 cited

Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation

Beatrice Savoldi, Marco Gaido, Luisa Bentivogli +2

Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages.…

cs.CL2021

Visualization: the missing factor in Simultaneous Speech Translation

Sara Papi, Matteo Negri, Marco Turchi

Simultaneous speech translation (SimulST) is the task in which output generation has to be performed on partial, incremental speech input. In recent years, SimulST has become popul…

cs.CL2021

Is "moby dick" a Whale or a Bird? Named Entities and Terminology in Speech Translation

Marco Gaido, Susana Rodríguez, Matteo Negri +2

Automatic translation systems are known to struggle with rare words. Among these, named entities (NEs) and domain-specific terms are crucial, since errors in their translation can…

cs.CL2021★ 8 cited

Speechformer: Reducing Information Loss in Direct Speech Translation

Sara Papi, Marco Gaido, Matteo Negri +1

Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation. However, Transformer's quadr…

cs.CL2021★ 1 cited

Simultaneous Speech Translation for Live Subtitling: from Delay to Display

Alina Karakanta, Sara Papi, Matteo Negri +1

With the increased audiovisualisation of communication, the need for live subtitles in multilingual events is more relevant than ever. In an attempt to automatise the process, we a…

cs.CL2021

Between Flexibility and Consistency: Joint Generation of Captions and Subtitles

Alina Karakanta, Marco Gaido, Matteo Negri +1

Speech translation (ST) has lately received growing interest for the generation of subtitles without the need for an intermediate source language transcription and timing (i.e. cap…

cs.CL2021★ 5 cited

Dealing with training and test segmentation mismatch: FBK@IWSLT2021

Sara Papi, Marco Gaido, Matteo Negri +1

This paper describes FBK's system submission to the IWSLT 2021 Offline Speech Translation task. We participated with a direct model, which is a Transformer-based architecture train…

cs.CL2021★ 1 cited

Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?

Luisa Bentivogli, Mauro Cettolo, Marco Gaido +4

Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions. In light of this steady p…

cs.CL2021

How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation

Marco Gaido, Beatrice Savoldi, Luisa Bentivogli +2

Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front. However…

cs.CL2021

Gender Bias in Machine Translation

Beatrice Savoldi, Marco Gaido, Luisa Bentivogli +2

Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information. However, it can suff…

cs.SD2021★ 16 cited

Beyond Voice Activity Detection: Hybrid Audio Segmentation for Direct Speech Translation

Marco Gaido, Matteo Negri, Mauro Cettolo +1

The audio segmentation mismatch between training data and those seen at run-time is a major problem in direct speech translation. Indeed, while systems are usually trained on manua…

cs.CL2021

Self-Learning for Zero Shot Neural Machine Translation

Surafel M. Lakew, Matteo Negri, Marco Turchi

Neural Machine Translation (NMT) approaches employing monolingual data are showing steady improvements in resource rich conditions. However, evaluations using real-world low-resour…