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Xuedong Huang

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3 papers

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NeurIPS Conference 2023 Conference Paper

ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation

Chenyang Le
Yao Qian
Long Zhou
Shujie Liu
Yanmin Qian
Michael Zeng
Xuedong Huang

Joint speech-language training is challenging due to the large demand for training data and GPU consumption, as well as the modality gap between speech and language. We present ComSL, a speech-language model built atop a composite architecture of public pre-trained speech-only and language-only models and optimized data-efficiently for spoken language tasks. Particularly, we propose to incorporate cross-modality learning into transfer learning and conduct them simultaneously for downstream tasks in a multi-task learning manner. Our approach has demonstrated effectiveness in end-to-end speech-to-text translation tasks, achieving a new state-of-the-art average BLEU score of 31. 5 on the multilingual speech to English text translation task for 21 languages, as measured on the public CoVoST2 evaluation set.

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AAAI Conference 2023 Conference Paper

i-Code: An Integrative and Composable Multimodal Learning Framework

Ziyi Yang
Yuwei Fang
Chenguang Zhu
Reid Pryzant
DongDong Chen
Yu Shi
Yichong Xu
Yao Qian

Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to one or two modalities. We present i-Code, a self-supervised pretraining framework where users may flexibly combine the modalities of vision, speech, and language into unified and general-purpose vector representations. In this framework, data from each modality are first given to pretrained single-modality encoders. The encoder outputs are then integrated with a multimodal fusion network, which uses novel merge- and co-attention mechanisms to effectively combine information from the different modalities. The entire system is pretrained end-to-end with new objectives including masked modality unit modeling and cross-modality contrastive learning. Unlike previous research using only video for pretraining, the i-Code framework can dynamically process single, dual, and triple-modality data during training and inference, flexibly projecting different combinations of modalities into a single representation space. Experimental results demonstrate how i-Code can outperform state-of-the-art techniques on five multimodal understanding tasks and single-modality benchmarks, improving by as much as 11% and demonstrating the power of integrative multimodal pretraining.

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IJCAI Conference 2022 Conference Paper

Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

Yichong Xu
Chenguang Zhu
Shuohang Wang
Siqi Sun
Hao Cheng
Xiaodong Liu
Jianfeng Gao
Pengcheng He

Most of today's AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains. In this paper, we propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear. By integrating external information into the prediction process, we hope to reduce the need for ever-larger models and increase the democratization of AI systems. We find that the proposed external attention mechanism can significantly improve the performance of existing AI systems, allowing practitioners to easily customize foundation AI models to many diverse downstream applications. In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities. The proposed system, Knowledgeable External Attention for commonsense Reasoning (KEAR), reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89. 4% in comparison to the human accuracy of 88. 9%.

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