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Found 17 papers, 7 papers with code
Maastricht University’s Multilingual Speech Translation System for IWSLT 2021
The task in this track is to build multilingual speech translation systems in supervised and zero-shot directions.
Maastricht University’s Large-Scale Multilingual Machine Translation System for WMT 2021
We present our development of the multilingual machine translation system for the large-scale multilingual machine translation task at WMT 2021.
Effective combination of pretrained models - KIT@IWSLT2022
Pretrained models in acoustic and textual modalities can potentially improve speech translation for both Cascade and End-to-end approaches.
Language-Independent Representations Improve Zero-Shot Summarization
In this work, we focus on summarization and tackle the problem through the lens of language-independent representations.
How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?
This gap is out of sync with recent progress in pretrained massively multilingual translation models.
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation
Secondly, we compare different approaches to low-latency speech translation using this framework.
KIT's Multilingual Speech Translation System for IWSLT 2023
In this paper, we describe our speech translation system for the multilingual track of IWSLT 2023, which evaluates translation quality on scientific conference talks.
Learning an Artificial Language for Knowledge-Sharing in Multilingual Translation
In this work, we discretize the encoder output latent space of multilingual models by assigning encoder states to entries in a codebook, which in effect represents source sentences in a new artificial language.
CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022
In this paper, we describe our submission to the Simultaneous Speech Translation at IWSLT 2022.
Tackling data scarcity in speech translation using zero-shot multilingual machine translation techniques
We investigate whether these ideas can be applied to speech translation, by building ST models trained on speech transcription and text translation data.
Cost-Effective Training in Low-Resource Neural Machine Translation
Although AL is shown to be helpful with large budgets, it is not enough to build high-quality translation systems in these low-resource conditions.
Direct Simultaneous Speech-to-Speech Translation with Variational Monotonic Multihead Attention
We present a direct simultaneous speech-to-speech translation (Simul-S2ST) model, Furthermore, the generation of translation is independent from intermediate text representations.
From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation
Speech-to-speech translation (S2ST) converts input speech to speech in another language.
Unsupervised Machine Translation On Dravidian Languages
We show that unifying the writing systems is essential in unsupervised translation between the Dravidian languages.
Improving Zero-Shot Translation by Disentangling Positional Information
The difficulty of generalizing to new translation directions suggests the model representations are highly specific to those language pairs seen in training.
Adapting End-to-End Speech Recognition for Readable Subtitles
The experiments show that with limited data far less than needed for training a model from scratch, we can adapt a Transformer-based ASR model to incorporate both transcription and compression capabilities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
On How2 English-Portuguese speech translation, we reduce latency to 0. 7 second (-84% rel.)
Sequence-To-Sequence Speech Recognition
speech-recognition
+1
