Search Results for author:
Found 15 papers, 14 papers with code
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #2 on
Multi-task Language Understanding
on MMLU
A Large-Scale Evaluation of Speech Foundation Models
In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech.
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities
In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.
textless-lib: a Library for Textless Spoken Language Processing
Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources.
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction
The lip-reading WER is further reduced to 26. 9% when using all 433 hours of labeled data from LRS3 and combined with self-training.
Ranked #3 on
Speech Recognition
on LRS3-TED
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
On the CoVoST-2 speech translation benchmark, we improve the previous state of the art by an average of 7. 4 BLEU over 21 translation directions into English.
Ranked #1 on
Language Identification
on VOXLINGUA107
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse methods such as BM25 in their ability to reliably match salient phrases and rare entities in the query and to generalize to out-of-domain data.
Ranked #2 on
Passage Retrieval
on EntityQuestions
Text-Free Prosody-Aware Generative Spoken Language Modeling
Generative Spoken Language Modeling (GSLM) \cite{Lakhotia2021} is the only prior work addressing the generative aspects of speech pre-training, which replaces text with discovered phone-like units for language modeling and shows the ability to generate meaningful novel sentences.
Ranked #8 on
Language Modelling
on SALMon
(using extra training data)
Domain-matched Pre-training Tasks for Dense Retrieval
Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks.
Ranked #2 on
Passage Retrieval
on Natural Questions
(using extra training data)
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation.
Ranked #5 on
Speech Recognition
on LibriSpeech test-other
SUPERB: Speech processing Universal PERformance Benchmark
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations
We propose using self-supervised discrete representations for the task of speech resynthesis.
Generative Spoken Language Modeling from Raw Audio
We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation.
Ranked #1 on
Resynthesis
on LibriSpeech
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for NLP tasks such as Question Answering (QA) and Fact Verification.
PyText: A Seamless Path from NLP research to production
We introduce PyText - a deep learning based NLP modeling framework built on PyTorch.
