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Found 6 papers, 1 papers with code
XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception
It is designed to maximize the benefits of limited multilingual AV pre-training data, by building on top of audio-only multilingual pre-training and simplifying existing pre-training schemes.
Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
Translations help people understand content written in another language.
Monotonic Simultaneous Translation with Chunk-wise Reordering and Refinement
Recent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a language pair whose word orders significantly differ.
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS
In general, the direct Speech-to-text translation (ST) is jointly trained with Automatic Speech Recognition (ASR), and Machine Translation (MT) tasks.
Ranked #1 on
Speech-to-Text Translation
on MuST-C EN->DE
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks
Inspired by these learning patterns in humans, we suggest a simple yet generic task aware framework to incorporate into existing joint learning strategies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Faster Re-translation Using Non-Autoregressive Model For Simultaneous Neural Machine Translation
The current re-translation approaches are based on autoregressive sequence generation models (ReTA), which generate tar-get tokens in the (partial) translation sequentially.
