Search Results for author:
Found 21 papers, 7 papers with code
Findings of the Third Workshop on Automatic Simultaneous Translation
This paper reports the results of the shared task we hosted on the Third Workshop of Automatic Simultaneous Translation (AutoSimTrans).
Mu$^{2}$SLAM: Multitask, Multilingual Speech and Language Models
We present Mu$^{2}$SLAM, a multilingual sequence-to-sequence model pre-trained jointly on unlabeled speech, unlabeled text and supervised data spanning Automatic Speech Recognition (ASR), Automatic Speech Translation (AST) and Machine Translation (MT), in over 100 languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Building Machine Translation Systems for the Next Thousand Languages
In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages.
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation
The dominant inductive bias applied to these models is a shared vocabulary and a shared set of parameters across languages; the inputs and labels corresponding to examples drawn from different language pairs might still reside in distinct sub-spaces.
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation
Self-supervised pre-training of text representations has been successfully applied to low-resource Neural Machine Translation (NMT).
Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions.
KoBE: Knowledge-Based Machine Translation Evaluation
Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task.
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT).
Ranked #22 on
Machine Translation
on WMT2014 English-German
Re-translation versus Streaming for Simultaneous Translation
There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available.
Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation
As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows.
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges
We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair.
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios.
Robust Neural Machine Translation with Doubly Adversarial Inputs
Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input.
Direct speech-to-speech translation with a sequence-to-sequence model
We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.
The Missing Ingredient in Zero-Shot Neural Machine Translation
Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation
In this paper, we demonstrate that using pre-trained MT or text-to-speech (TTS) synthesis models to convert weakly supervised data into speech-to-translation pairs for ST training can be more effective than multi-task learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation
However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zero-shot multilinguality in other NLP tasks.
Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering.
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures.
Ranked #26 on
Machine Translation
on WMT2014 English-French
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.
Ranked #35 on
Machine Translation
on WMT2014 English-French
