40 citations · 110 across the 18 of their papers we have counts for
18 papers
Multi-Grained Multimodal Interaction Network for Entity Linking
Pengfei Luo, Tong Xu, Shiwei Wu +3
Multimodal entity linking (MEL) task, which aims at resolving ambiguous mentions to a multimodal knowledge graph, has attracted wide attention in recent years. Though large efforts…
End-to-End Word-Level Pronunciation Assessment with MASK Pre-training
Yukang Liang, Kaitao Song, Shaoguang Mao +6
Pronunciation assessment is a major challenge in the computer-aided pronunciation training system, especially at the word (phoneme)-level. To obtain word (phoneme)-level scores, cu…
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA
Yongxin Zhu, Zhen Liu, Yukang Liang +4
In this paper, we propose a novel multi-modal framework for Scene Text Visual Question Answering (STVQA), which requires models to read scene text in images for question answering.…
Empowering Diffusion Models on the Embedding Space for Text Generation
Zhujin Gao, Junliang Guo, Xu Tan +4
Diffusion models have achieved state-of-the-art synthesis quality on both visual and audio tasks, and recent works further adapt them to textual data by diffusing on the embedding…
Bridging Music and Text with Crowdsourced Music Comments: A Sequence-to-Sequence Framework for Thematic Music Comments Generation
Peining Zhang, Junliang Guo, Linli Xu +2
We consider a novel task of automatically generating text descriptions of music. Compared with other well-established text generation tasks such as image caption, the scarcity of w…
Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation
Jiquan Li, Junliang Guo, Yongxin Zhu +4
The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years. While one of the key p…
Towards Variable-Length Textual Adversarial Attacks
Junliang Guo, Zhirui Zhang, Linlin Zhang +4
Adversarial attacks have shown the vulnerability of machine learning models, however, it is non-trivial to conduct textual adversarial attacks on natural language processing tasks…
Incorporating BERT into Parallel Sequence Decoding with Adapters
Junliang Guo, Zhirui Zhang, Linli Xu +3
While large scale pre-trained language models such as BERT have achieved great success on various natural language understanding tasks, how to efficiently and effectively incorpora…
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period
Shuheng Shen, Yifei Cheng, Jingchang Liu +1
Distributed parallel stochastic gradient descent algorithms are workhorses for large scale machine learning tasks. Among them, local stochastic gradient descent (Local SGD) has att…
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation
Junliang Guo, Xu Tan, Linli Xu +3
Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup b…
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent
Shuheng Shen, Linli Xu, Jingchang Liu +2
With the increase in the amount of data and the expansion of model scale, distributed parallel training becomes an important and successful technique to address the optimization ch…
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
Junliang Guo, Xu Tan, Di He +3
Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at th…
Asynchronous Stochastic Composition Optimization with Variance Reduction
Shuheng Shen, Linli Xu, Jingchang Liu +2
Composition optimization has drawn a lot of attention in a wide variety of machine learning domains from risk management to reinforcement learning. Existing methods solving the com…
Accelerating Stochastic Gradient Descent Using Antithetic Sampling
Jingchang Liu, Linli Xu
(Mini-batch) Stochastic Gradient Descent is a popular optimization method which has been applied to many machine learning applications. But a rather high variance introduced by the…
How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks
Linli Xu, Liang Jiang, Chuan Qin +2
With the recent advances of neural models and natural language processing, automatic generation of classical Chinese poetry has drawn significant attention due to its artistic and…
SPINE: Structural Identity Preserved Inductive Network Embedding
Junliang Guo, Linli Xu, Jingchang Liu
Recent advances in the field of network embedding have shown that low-dimensional network representation is playing a critical role in network analysis. Most existing network embed…
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
Junliang Guo, Linli Xu, Xunpeng Huang +1
Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existin…
Make Workers Work Harder: Decoupled Asynchronous Proximal Stochastic Gradient Descent
Yitan Li, Linli Xu, Xiaowei Zhong +1
Asynchronous parallel optimization algorithms for solving large-scale machine learning problems have drawn significant attention from academia to industry recently. This paper prop…