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Found 16 papers, 1 papers with code
Multimodal Modeling For Spoken Language Identification
Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance.
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
SQuId: Measuring Speech Naturalness in Many Languages
The main insight is that training one model on many locales consistently outperforms mono-locale baselines.
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation
We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation.
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark.
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.
XTREME-S: Evaluating Cross-lingual Speech Representations
Covering 102 languages from 10+ language families, 3 different domains and 4 task families, XTREME-S aims to simplify multilingual speech representation evaluation, as well as catalyze research in "universal" speech representation learning.
mSLAM: Massively multilingual joint pre-training for speech and text
We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unlabeled speech and text in multiple languages.
Ranked #1 on
Spoken language identification
on FLEURS
(using extra training data)
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training
We build a single encoder with the BERT objective on unlabeled text together with the w2v-BERT objective on unlabeled speech.
Improving Multilingual Models with Language-Clustered Vocabularies
State-of-the-art multilingual models depend on vocabularies that cover all of the languages the model will expect to see at inference time, but the standard methods for generating those vocabularies are not ideal for massively multilingual applications.
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition
Transformer-based models have achieved stateof-the-art results in many tasks in natural language processing.
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation
The recently proposed massively multilingual neural machine translation (NMT) system has been shown to be capable of translating over 100 languages to and from English within a single model.
Small and Practical BERT Models for Sequence Labeling
We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU.
A Fast, Compact, Accurate Model for Language Identification of Codemixed Text
We address fine-grained multilingual language identification: providing a language code for every token in a sentence, including codemixed text containing multiple languages.
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
