no code implementations • EMNLP (IWSLT) 2019 • Mattia A. Di Gangi, Matteo Negri, Viet Nhat Nguyen, Amirhossein Tebbifakhr, Marco Turchi
On the training side, we focused on data augmentation techniques recently proposed for ST and automatic speech recognition (ASR).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • VarDial (COLING) 2020 • Amirhossein Tebbifakhr, Matteo Negri, Marco Turchi
In this work, we tackle the problem in a multilingual setting where a single NMT model translates from multiple languages for downstream automatic processing in the target language.
no code implementations • EAMT 2020 • Amirhossein Tebbifakhr, Matteo Negri, Marco Turchi
We address this problem by proposing a multi-task approach to machine-oriented NMT adaptation, which is capable to serve multiple downstream tasks with a single system.
no code implementations • IJCNLP 2019 • Amirhossein Tebbifakhr, Luisa Bentivogli, Matteo Negri, Marco Turchi
Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback.
no code implementations • WS 2019 • Amirhossein Tebbifakhr, Matteo Negri, Marco Turchi
For this purpose, following the common approach in multilingual NMT, we prepend a special token to the beginning of both the source text and the MT output indicating the required amount of post-editing.
no code implementations • WS 2018 • Amirhossein Tebbifakhr, Ruchit Agrawal, Matteo Negri, Marco Turchi
In the first subtask, our system improves over the baseline up to -5. 3 TER and +8. 23 BLEU points ranking second out of 11 submitted runs.
no code implementations • 25 May 2016 • Javid Dadashkarimi, Mahsa S. Shahshahani, Amirhossein Tebbifakhr, Heshaam Faili, Azadeh Shakery
Using top-ranked documents in response to a query has been shown to be an effective approach to improve the quality of query translation in dictionary-based cross-language information retrieval.