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Found 7 papers, 2 papers with code
FBK’s Neural Machine Translation Systems for IWSLT 2016
They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words.
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19
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
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021
This paper describes the offline and simultaneous speech translation systems developed at AppTek for IWSLT 2021.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
On Knowledge Distillation for Direct Speech Translation
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).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
On the Importance of Word Boundaries in Character-level Neural Machine Translation
Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality.
Neural Text Simplification in Low-Resource Conditions Using Weak Supervision
Inspired by the machine translation field, in which synthetic parallel pairs generated from monolingual data yield significant improvements to neural models, in this paper we exploit large amounts of heterogeneous data to automatically select simple sentences, which are then used to create synthetic simplification pairs.
MuST-C: a Multilingual Speech Translation Corpus
Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
