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Found 20 papers, 9 papers with code
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation.
Merging Text Transformer Models from Different Initializations
Recent work on one-shot permutation-based model merging has shown impressive low- or zero-barrier mode connectivity between models from completely different initializations.
SpiRit-LM: Interleaved Spoken and Written Language Model
We introduce SPIRIT-LM, a foundation multimodal language model that freely mixes text and speech.
Seamless: Multilingual Expressive and Streaming Speech Translation
In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion.
Added Toxicity Mitigation at Inference Time for Multimodal and Massively Multilingual Translation
MinTox uses a toxicity detection classifier which is multimodal (speech and text) and works in languages at scale.
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages?
Ranked #1 on
Speech-to-Speech Translation
on CVSS
(using extra training data)
Automatic Speech Recognition
Speech-to-Speech Translation
+3
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity
Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computational requirements per token.
Efficiently Upgrading Multilingual Machine Translation Models to Support More Languages
With multilingual machine translation (MMT) models continuing to grow in size and number of supported languages, it is natural to reuse and upgrade existing models to save computation as data becomes available in more languages.
Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation
Sparsely gated Mixture of Experts (MoE) models have been shown to be a compute-efficient method to scale model capacity for multilingual machine translation.
Causes and Cures for Interference in Multilingual Translation
Multilingual machine translation models can benefit from synergy between different language pairs, but also suffer from interference.
No Language Left Behind: Scaling Human-Centered Machine Translation
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
Ranked #1 on
Machine Translation
on IWSLT2017 French-English
(SacreBLEU metric)
Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English
We conduct in this work an evaluation study comparing offline and online neural machine translation architectures.
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation.
Efficient Wait-k Models for Simultaneous Machine Translation
We also show that the 2D-convolution architecture is competitive with Transformers for simultaneous translation of spoken language.
Depth-Adaptive Transformer
State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process.
Improved Training Techniques for Online Neural Machine Translation
We investigate the sensitivity of such models to the value of k that is used during training and when deploying the model, and the effect of updating the hidden states in transformer models as new source tokens are read.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding.
Ranked #2 on
Machine Translation
on IWSLT2015 German-English
Token-level and sequence-level loss smoothing for RNN language models
We extend this approach to token-level loss smoothing, and propose improvements to the sequence-level smoothing approach.
