Search Results for author:
Found 10 papers, 6 papers with code
Toward Joint Language Modeling for Speech Units and Text
However, in the field of language modeling, very little effort has been made to model them jointly.
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs.
Ranked #2 on
Text-to-Image Generation
on MS COCO
Scaling Speech Technology to 1,000+ Languages
Expanding the language coverage of speech technology has the potential to improve access to information for many more people.
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
Current self-supervised learning algorithms are often modality-specific and require large amounts of computational resources.
Ranked #93 on
Image Classification
on ImageNet
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind.
Ranked #1 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
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
(using extra training data)
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy.
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing
An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance $x$, predicting a frame's length |y|, and decoding a |y|-sized frame with utterance and ontology tokens.
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models.
Lightweight Convolutional Representations for On-Device Natural Language Processing
The increasing computational and memory complexities of deep neural networks have made it difficult to deploy them on low-resource electronic devices (e. g., mobile phones, tablets, wearables).
