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Found 14 papers, 1 papers with code
An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task
This paper describes our submission to the WMT20 news translation shared task in English to Japanese direction.
Language Model Augmented Monotonic Attention for Simultaneous Translation
The state-of-the-art adaptive policies for Simultaneous Neural Machine Translation (SNMT) use monotonic attention to perform read/write decisions based on the partial source and target sequences.
Label-Free Multi-Domain Machine Translation with Stage-wise Training
Our model is composed of three parts: a backbone model, a domain discriminator taking responsibility to discriminate data from different domains, and a set of experts that transfer the decoded features from generic to specific.
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.
Decision Attentive Regularization to Improve Simultaneous Speech Translation Systems
Hence, read/write decision policy remains the same across different input modalities, i. e., speech and text.
Infusing Future Information into Monotonic Attention Through Language Models
Simultaneous neural machine translation(SNMT) models start emitting the target sequence before they have processed the source sequence.
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.
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation
Our analysis shows that for a given number of quantization bits, each block of Transformer contributes to translation quality and inference computations in different manners.
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning
In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge.
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning
In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task.
Ranked #3 on
Speech-to-Text Translation
on MuST-C EN->DE
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning
In the meta-learning phase, the parameters of the model are exposed to vast amounts of speech transcripts (e. g., English ASR) and text translations (e. g., English-German MT).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Look Harder: A Neural Machine Translation Model with Hard Attention
Soft-attention based Neural Machine Translation (NMT) models have achieved promising results on several translation tasks.
