342 citations · 389 across the 17 of their papers we have counts for
7 papers · 1 filter
Florence: A New Foundation Model for Computer Vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen +20
Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human visio…
Optimizing Alignment of Speech and Language Latent Spaces for End-to-End Speech Recognition and Understanding
Wei Wang, Shuo Ren, Yao Qian +4
The advances in attention-based encoder-decoder (AED) networks have brought great progress to end-to-end (E2E) automatic speech recognition (ASR). One way to further improve the pe…
A Joint and Domain-Adaptive Approach to Spoken Language Understanding
Linhao Zhang, Yu Shi, Linjun Shou +3
Spoken Language Understanding (SLU) is composed of two subtasks: intent detection (ID) and slot filling (SF). There are two lines of research on SLU. One jointly tackles these two…
Transformer-F: A Transformer network with effective methods for learning universal sentence representation
Yu Shi
The Transformer model is widely used in natural language processing for sentence representation. However, the previous Transformer-based models focus on function words that have li…
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
Junwei Liao, Yu Shi, Ming Gong +5
Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging t…
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders
Junwei Liao, Yu Shi, Ming Gong +3
Recently, universal neural machine translation (NMT) with shared encoder-decoder gained good performance on zero-shot translation. Unlike universal NMT, jointly trained language-sp…
Speech-language Pre-training for End-to-end Spoken Language Understanding
Yao Qian, Ximo Bian, Yu Shi +4
End-to-end (E2E) spoken language understanding (SLU) can infer semantics directly from speech signal without cascading an automatic speech recognizer (ASR) with a natural language…