Search Results for author:
Found 52 papers, 10 papers with code
Sequential Dialogue Context Modeling for Spoken Language Understanding
We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history.
Goal-Oriented Dialogue Systems
Spoken Language Understanding
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems.
Building a Conversational Agent Overnight with Dialogue Self-Play
We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains.
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
Training Deeper Neural Machine Translation Models with Transparent Attention
While current state-of-the-art NMT models, such as RNN seq2seq and Transformers, possess a large number of parameters, they are still shallow in comparison to convolutional models used for both text and vision applications.
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.
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.
Ranked #4 on
Fine-Grained Image Classification
on Birdsnap
(using extra training data)
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.
Non-Parametric Adaptation for Neural Machine Translation
Neural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process.
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.
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.
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.
Investigating Multilingual NMT Representations at Scale
Multilingual Neural Machine Translation (NMT) models have yielded large empirical success in transfer learning settings.
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model
Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic speech recognition (ASR) coverage of the world's languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Simple, Scalable Adaptation for Neural Machine Translation
We evaluate our approach on two tasks: (i) Domain Adaptation and (ii) Massively Multilingual NMT.
Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation
In particular, we consider three distinct approaches to generate the missing context: using random contexts, applying a copy heuristic or generating it with a language model.
Faster Transformer Decoding: N-gram Masked Self-Attention
Motivated by the fact that most of the information relevant to the prediction of target tokens is drawn from the source sentence $S=s_1, \ldots, s_S$, we propose truncating the target-side window used for computing self-attention by making an $N$-gram assumption.
Controlling Computation versus Quality for Neural Sequence Models
Further, methods that adapt the amount of computation to the example focus on finding a fixed inference-time computational graph per example, ignoring any external computational budgets or varying inference time limitations.
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation
Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged.
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus
Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context.
Exploring Routing Strategies for Multilingual Mixture-of-Experts Models
Sparsely-Gated Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation.
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
Our study further verifies the trade-off between the shared capacity and LS capacity for multilingual translation.
Gradient-guided Loss Masking for Neural Machine Translation
To mitigate the negative effect of low quality training data on the performance of neural machine translation models, most existing strategies focus on filtering out harmful data before training starts.
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages.
Scaling Laws for Neural Machine Translation
We show that cross-entropy loss as a function of model size follows a certain scaling law.
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents
Using simple concatenation-based DocNMT, we explore the effect of 3 factors on the transfer: the number of teacher languages with document level data, the balance between document and sentence level data at training, and the data condition of parallel documents (genuine vs. backtranslated).
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference
On WMT, our task-MoE with 32 experts (533M parameters) outperforms the best performing token-level MoE model (token-MoE) by +1. 0 BLEU on average across 30 language pairs.
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.
Joint Unsupervised and Supervised Training for Multilingual ASR
Our average WER of all languages outperforms average monolingual baseline by 33. 3%, and the state-of-the-art 2-stage XLSR by 32%.
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning
While recent progress in massively multilingual MT is one step closer to reaching this goal, it is becoming evident that extending a multilingual MT system simply by training on more parallel data is unscalable, since the availability of labeled data for low-resource and non-English-centric language pairs is forbiddingly limited.
Examining Scaling and Transfer of Language Model Architectures for Machine Translation
Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single stack of layers, and encoder-decoder models (EncDec) that utilize separate layer stacks for input and output processing.
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.
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.
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.
Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation
End-to-end speech-to-speech translation (S2ST) without relying on intermediate text representations is a rapidly emerging frontier of research.
MAESTRO: Matched Speech Text Representations through Modality Matching
Self-supervised learning from speech signals aims to learn the latent structure inherent in the signal, while self-supervised learning from text attempts to capture lexical information.
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.
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
SQuId: Measuring Speech Naturalness in Many Languages
The main insight is that training one model on many locales consistently outperforms mono-locale baselines.
JOIST: A Joint Speech and Text Streaming Model For ASR
In addition, we explore JOIST using a streaming E2E model with an order of magnitude more data, which are also novelties compared to previous works.
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR
First, we show that by combining speech representations with byte-level text representations and use of language embeddings, we can dramatically reduce the Character Error Rate (CER) on languages with no supervised speech from 64. 8\% to 30. 8\%, a relative reduction of 53\%.
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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
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
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Experiments show that Miipher (i) is robust against various audio degradation and (ii) enable us to train a high-quality text-to-speech (TTS) model from restored speech samples collected from the Web.
Understanding Shared Speech-Text Representations
Recently, a number of approaches to train speech models by incorpo-rating text into end-to-end models have been developed, with Mae-stro advancing state-of-the-art automatic speech recognition (ASR)and Speech Translation (ST) performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound quality improved.
Label Aware Speech Representation Learning For Language Identification
In this paper, we propose a novel framework of combining self-supervised representation learning with the language label information for the pre-training task.
AudioPaLM: A Large Language Model That Can Speak and Listen
AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2.
Multimodal Modeling For Spoken Language Identification
Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we present the latest model of the Gemini family, Gemini 1. 5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Ranked #18 on
Code Generation
on HumanEval
