33 citations · 121 across the 12 of their papers we have counts for
8 papers · 2 filters
Controlling the Output Length of Neural Machine Translation
Surafel Melaku Lakew, Mattia Di Gangi, Marcello Federico
The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be…
Instance-Based Model Adaptation For Direct Speech Translation
Mattia Antonino Di Gangi, Viet-Nhat Nguyen, Matteo Negri +1
Despite recent technology advancements, the effectiveness of neural approaches to end-to-end speech-to-text translation is still limited by the paucity of publicly available traini…
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts
Mattia Antonino Di Gangi, Robert Enyedi, Alessandra Brusadin +1
Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is no…
On the Importance of Word Boundaries in Character-level Neural Machine Translation
Duygu Ataman, Orhan Firat, Mattia A. Di Gangi +2
Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and…
One-To-Many Multilingual End-to-end Speech Translation
Mattia Antonino Di Gangi, Matteo Negri, Marco Turchi
Nowadays, training end-to-end neural models for spoken language translation (SLT) still has to confront with extreme data scarcity conditions. The existing SLT parallel corpora are…
Multilingual Neural Machine Translation for Zero-Resource Languages
Surafel M. Lakew, Marcello Federico, Matteo Negri +1
In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine t…
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors
Nicholas Ruiz, Mattia Antonino Di Gangi, Nicola Bertoldi +1
Machine translation systems are conventionally trained on textual resources that do not model phenomena that occur in spoken language. While the evaluation of neural machine transl…
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection
Mattia Antonino Di Gangi, Giosué Lo Bosco, Giovanni Pilato
Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for aut…