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Found 16 papers, 10 papers with code
BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training
In this study, we systematically analyze and compare various mainstream and cutting-edge automatic metrics from the perspective of their guidance for training machine translation systems.
Recent Advances in Direct Speech-to-text Translation
Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly.
Improving speech translation by fusing speech and text
To tackle these gaps, we propose \textbf{F}use-\textbf{S}peech-\textbf{T}ext (\textbf{FST}), a cross-modal model which supports three distinct input modalities for translation: speech, text, and fused speech-text.
Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation
Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks.
WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research
To address this data scarcity issue, we introduce WavCaps, the first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k audio clips with paired captions.
Ranked #1 on
Zero-Shot Environment Sound Classification
on ESC-50
(using extra training data)
GigaST: A 10,000-hour Pseudo Speech Translation Corpus
The training set is translated by a strong machine translation system and the test set is translated by human.
Secoco: Self-Correcting Encoding for Neural Machine Translation
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting predictors.
Autocorrect in the Process of Translation --- Multi-task Learning Improves Dialogue Machine Translation
Automatic translation of dialogue texts is a much needed demand in many real life scenarios.
The Volctrans Neural Speech Translation System for IWSLT 2021
For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.
Counter-Interference Adapter for Multilingual Machine Translation
Developing a unified multilingual model has long been a pursuit for machine translation.
Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Automatic translation of dialogue texts is a much needed demand in many real life scenarios.
Finding Sparse Structures for Domain Specific Neural Machine Translation
Neural machine translation often adopts the fine-tuning approach to adapt to specific domains.
NeurST: Neural Speech Translation Toolkit
NeurST is an open-source toolkit for neural speech translation.
Ranked #1 on
Speech-to-Text Translation
on libri-trans
Rethinking Document-level Neural Machine Translation
This paper does not aim at introducing a novel model for document-level neural machine translation.
Kernelized Bayesian Softmax for Text Generation
Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase.
Towards Making the Most of BERT in Neural Machine Translation
Our experiments in machine translation show CTNMT gains of up to 3 BLEU score on the WMT14 English-German language pair which even surpasses the previous state-of-the-art pre-training aided NMT by 1. 4 BLEU score.
