no code implementations • EMNLP 2021 • Chengyu Wang, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, Yin Zhang
For tasks related to distant domains with different class label sets, PLMs may memorize non-transferable knowledge for the target domain and suffer from negative transfer.
2 code implementations • EMNLP 2021 • Chengyu Wang, Jianing Wang, Minghui Qiu, Jun Huang, Ming Gao
Based on continuous prompt embeddings, we propose TransPrompt, a transferable prompting framework for few-shot learning across similar tasks.
no code implementations • 26 Jul 2022 • Jiang Bian, Qingzhong Wang, Haoyi Xiong, Jun Huang, Chen Liu, Xuhong LI, Jun Cheng, Jun Zhao, Feixiang Lu, Dejing Dou
While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging.
1 code implementation • 6 May 2022 • Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, Ming Gao
Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-trained Language Models (PLMs).
1 code implementation • 30 Apr 2022 • Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei LI, Jianing Wang, Ming Wang, Jun Huang, Wei Lin
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP).
1 code implementation • 1 Apr 2022 • Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, Jun Huang
Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data.
2 code implementations • 14 Dec 2021 • Runxin Xu, Fuli Luo, Chengyu Wang, Baobao Chang, Jun Huang, Songfang Huang, Fei Huang
Unified in contrastive learning, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge.
1 code implementation • 2 Dec 2021 • Taolin Zhang, Chengyu Wang, Nan Hu, Minghui Qiu, Chengguang Tang, Xiaofeng He, Jun Huang
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.
no code implementations • 16 Nov 2021 • Shubo Lv, Yihui Fu, Mengtao Xing, Jiayao Sun, Lei Xie, Jun Huang, Yannan Wang, Tao Yu
In speech enhancement, complex neural network has shown promising performance due to their effectiveness in processing complex-valued spectrum.
no code implementations • 11 Aug 2021 • Xiaoxia Xu, Qimei Chen, Hao Jiang, Jun Huang
Our aim for the proposed coexistence network is to maximize the spectral efficiency while ensuring the strict NR-U delay requirement and the WiGig transmission performance in real time environments.
1 code implementation • 22 Jun 2021 • Xiwen Qu, Hao Che, Jun Huang, Linchuan Xu, Xiao Zheng
To this end, this paper designs a Multi-layered Semantic Representation Network (MSRN) which discovers both local and global semantics of labels through modeling label correlations and utilizes the label semantics to guide the semantic representations learning at multiple layers through an attention mechanism.
Ranked #5 on
Multi-Label Classification
on PASCAL VOC 2007
2 code implementations • 12 May 2021 • Abhinav Jangda, Jun Huang, Guodong Liu, Amir Hossein Nodehi Sabet, Saeed Maleki, Youshan Miao, Madanlal Musuvathi, Todd Mytkowicz, Olli Sarikivi
Therefore, we present CoCoNeT, with a DSL to express a program with both computation and communication.
no code implementations • 4 Jan 2021 • Guoxu Feng, Jun Huang
This paper reviews the history and origin of the Abraham-Minkowski controversy and points out that it is a continuation of the controversy over the speed of light in medium.
Optics
1 code implementation • ACL 2021 • Haojie Pan, Chengyu Wang, Minghui Qiu, Yichang Zhang, Yaliang Li, Jun Huang
We argue that training a teacher with transferable knowledge digested across domains can achieve better generalization capability to help knowledge distillation.
no code implementations • 25 Nov 2020 • Haojie Pan, Cen Chen, Minghui Qiu, Liu Yang, Feng Ji, Jun Huang, Haiqing Chen
More specifically, we proposed a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT based response ranker to rank the PRF-enhanced responses.
2 code implementations • 18 Nov 2020 • Minghui Qiu, Peng Li, Chengyu Wang, Hanjie Pan, Ang Wang, Cen Chen, Xianyan Jia, Yaliang Li, Jun Huang, Deng Cai, Wei Lin
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.
Conversational Question Answering
Natural Language Processing
+1
no code implementations • 28 Oct 2020 • Yiwu Yao, Yuchao Li, Chengyu Wang, Tianhang Yu, Houjiang Chen, Xiaotang Jiang, Jun Yang, Jun Huang, Wei Lin, Hui Shu, Chengfei Lv
The intensive computation of Automatic Speech Recognition (ASR) models obstructs them from being deployed on mobile devices.
no code implementations • 14 Sep 2020 • Chengyu Wang, Mengli Cheng, Xu Hu, Jun Huang
We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing audio data at scale.
1 code implementation • 9 Sep 2020 • Mengli Cheng, Minghui Qiu, Xing Shi, Jun Huang, Wei. Lin
Existing learning based methods for text labeling task usually require a large amount of labeled examples to train a specific model for each type of document.
1 code implementation • Findings (ACL) 2021 • Taolin Zhang, Chengyu Wang, Minghui Qiu, Bite Yang, Xiaofeng He, Jun Huang
In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving.
no code implementations • 4 Aug 2020 • Mengli Cheng, Chengyu Wang, Xu Hu, Jun Huang, Xiaobo Wang
Building Automatic Speech Recognition (ASR) systems from scratch is significantly challenging, mostly due to the time-consuming and financially-expensive process of annotating a large amount of audio data with transcripts.
Automatic Speech Recognition
Optical Character Recognition
+2
no code implementations • 31 Jul 2020 • Linchuan Xu, Jun Huang, Atsushi Nitanda, Ryo Asaoka, Kenji Yamanishi
In this paper, we thus propose a novel global spatial attention mechanism in CNNs mainly for medical image classification.
2 code implementations • EMNLP 2020 • Chengyu Wang, Minghui Qiu, Jun Huang, Xiaofeng He
In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), served as a meta-learner to solve a group of similar NLP tasks for neural language models.
no code implementations • CVPR 2020 • Qiangpeng Yang, Hongsheng Jin, Jun Huang, Wei. Lin
First, a novel text swapping network is proposed to replace text labels only in the foreground image.
no code implementations • 25 Feb 2020 • Chengyu Wang, Minghui Qiu, Jun Huang, Xiaofeng He
We further combine a meta-learning process over the auxiliary task distribution and supervised learning to train the neural lexical relation classifier.
1 code implementation • 13 Jan 2020 • Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei. Lin, Jingren Zhou
Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.
1 code implementation • 7 Feb 2019 • Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F. Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel Salathé, Sergey Levine, Scott Delp
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
no code implementations • 30 Dec 2018 • Chen Qu, Feng Ji, Minghui Qiu, Liu Yang, Zhiyu Min, Haiqing Chen, Jun Huang, W. Bruce Croft
Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector.
no code implementations • 29 Aug 2018 • Cen Chen, Minghui Qiu, Yinfei Yang, Jun Zhou, Jun Huang, Xiaolong Li, Forrest Bao
Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions.
no code implementations • ACL 2018 • Minghui Qiu, Liu Yang, Feng Ji, Weipeng Zhao, Wei Zhou, Jun Huang, Haiqing Chen, W. Bruce Croft, Wei. Lin
Building multi-turn information-seeking conversation systems is an important and challenging research topic.
1 code implementation • 1 May 2018 • Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen
Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.
no code implementations • 12 Jan 2018 • Feng-Lin Li, Minghui Qiu, Haiqing Chen, Xiongwei Wang, Xing Gao, Jun Huang, Juwei Ren, Zhongzhou Zhao, Weipeng Zhao, Lei Wang, Guwei Jin, Wei Chu
We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce.
1 code implementation • 23 Nov 2017 • Jianfei Yu, Minghui Qiu, Jing Jiang, Jun Huang, Shuangyong Song, Wei Chu, Haiqing Chen
In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource- poor target domain.
no code implementations • ACL 2017 • Minghui Qiu, Feng-Lin Li, Siyu Wang, Xing Gao, Yan Chen, Weipeng Zhao, Haiqing Chen, Jun Huang, Wei Chu
We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models.