Search Results for author: Wenbiao Ding

Found 25 papers, 15 papers with code

Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data

1 code implementation10 Apr 2022 Yu Kang, Tianqiao Liu, Hang Li, Yang Hao, Wenbiao Ding

Our pre-training framework consists of the following components: (1) Intra-modal Denoising Auto-Encoding (IDAE), which is able to reconstruct input text (audio) representations from a noisy version of itself.

Denoising

Learning with Noisy Correspondence for Cross-modal Matching

1 code implementation NeurIPS 2021 Zhenyu Huang, guocheng niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, Xi Peng

Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.

Image-text matching Memorization +2

An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms

no code implementations15 Jul 2021 Jiahao Chen, Hang Li, Wenbiao Ding, Zitao Liu

In this paper, we propose a simple yet effective solution to build practical teacher recommender systems for online one-on-one classes.

Recommendation Systems

Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining

1 code implementation15 Jul 2021 Guowei Xu, Wenbiao Ding, Weiping Fu, Zhongqin Wu, Zitao Liu

Despite that pre-trained models achieve state-of-the-art performance in many NLP benchmarks, we prove that they are not robust to noisy texts generated by real OCR engines.

Optical Character Recognition Optical Character Recognition (OCR) +2

Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models

1 code implementation15 Jul 2021 Yang Hao, Hang Li, Wenbiao Ding, Zhongqin Wu, Jiliang Tang, Rose Luckin, Zitao Liu

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits.

Multi-Task Learning

Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models

1 code implementation15 Jul 2021 Qiongqiong Liu, Tianqiao Liu, Jiafu Zhao, Qiang Fang, Wenbiao Ding, Zhongqin Wu, Feng Xia, Jiliang Tang, Zitao Liu

Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.

Sentence Sentence Completion

A Multimodal Machine Learning Framework for Teacher Vocal Delivery Evaluation

1 code implementation15 Jul 2021 Hang Li, Yu Kang, Yang Hao, Wenbiao Ding, Zhongqin Wu, Zitao Liu

The quality of vocal delivery is one of the key indicators for evaluating teacher enthusiasm, which has been widely accepted to be connected to the overall course qualities.

BIG-bench Machine Learning

Temporal-aware Language Representation Learning From Crowdsourced Labels

1 code implementation ACL (RepL4NLP) 2021 Yang Hao, Xiao Zhai, Wenbiao Ding, Zitao Liu

A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability.

Representation Learning

Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension

1 code implementation15 Jul 2021 Shiting Xu, Guowei Xu, Peilei Jia, Wenbiao Ding, Zhongqin Wu, Zitao Liu

A TR writing question may include multiple requirements and a high-quality essay must respond to each requirement thoroughly and accurately.

Machine Reading Comprehension

Towards the Memorization Effect of Neural Networks in Adversarial Training

no code implementations9 Jun 2021 Han Xu, Xiaorui Liu, Wentao Wang, Wenbiao Ding, Zhongqin Wu, Zitao Liu, Anil Jain, Jiliang Tang

In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.

Adversarial Robustness Memorization

Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence

1 code implementation ACL 2021 Jian Guan, Xiaoxi Mao, Changjie Fan, Zitao Liu, Wenbiao Ding, Minlie Huang

Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation.

Semantic Similarity Semantic Textual Similarity +2

OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics

1 code implementation ACL 2021 Jian Guan, Zhexin Zhang, Zhuoer Feng, Zitao Liu, Wenbiao Ding, Xiaoxi Mao, Changjie Fan, Minlie Huang

Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation.

Story Generation

Representation Learning from Limited Educational Data with Crowdsourced Labels

1 code implementation23 Sep 2020 Wentao Wang, Guowei Xu, Wenbiao Ding, Gale Yan Huang, Guoliang Li, Jiliang Tang, Zitao Liu

Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines.

Face Recognition Machine Translation +1

Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes

no code implementations16 May 2020 Shiting Xu, Wenbiao Ding, Zitao Liu

Online one-on-one class is created for highly interactive and immersive learning experience.

Siamese Neural Networks for Class Activity Detection

no code implementations15 May 2020 Hang Li, Zhiwei Wang, Jiliang Tang, Wenbiao Ding, Zitao Liu

Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms.

Action Detection Activity Detection

NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels

2 code implementations21 Mar 2020 Yang Hao, Wenbiao Ding, Zitao Liu

Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc.

Representation Learning

Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach

no code implementations21 Mar 2020 Hang Li, Wenbiao Ding, Zitao Liu

We conduct a wide range of offline and online experiments to demonstrate the effectiveness of our approach.

BIG-bench Machine Learning

Multimodal Learning For Classroom Activity Detection

no code implementations22 Oct 2019 Hang Li, Yu Kang, Wenbiao Ding, Song Yang, Songfan Yang, Gale Yan Huang, Zitao Liu

The experimental results demonstrate the benefits of our approach on learning attention based neural network from classroom data with different modalities, and show our approach is able to outperform state-of-the-art baselines in terms of various evaluation metrics.

Action Detection Activity Detection

Automatic Short Answer Grading via Multiway Attention Networks

no code implementations23 Sep 2019 Tiaoqiao Liu, Wenbiao Ding, Zhiwei Wang, Jiliang Tang, Gale Yan Huang, Zitao Liu

Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads.

A Multimodal Alerting System for Online Class Quality Assurance

no code implementations1 Sep 2019 Jiahao Chen, Hang Li, Wenxin Wang, Wenbiao Ding, Gale Yan Huang, Zitao Liu

To warn the unqualified instructors and ensure the overall education quality, we build a monitoring and alerting system by utilizing multimodal information from the online environment.

Dolphin: A Spoken Language Proficiency Assessment System for Elementary Education

no code implementations1 Aug 2019 Wenbiao Ding, Guowei Xu, Tianqiao Liu, Weiping Fu, Yujia Song, Chaoyou Guo, Cong Kong, Songfan Yang, Gale Yan Huang, Zitao Liu

In our offline experiments, we show that Dolphin improves both phonological fluency and semantic relevance evaluation performance when compared to state-of-the-art baselines on real-world educational data sets.

Math

Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study

1 code implementation18 Jul 2019 Guowei Xu, Wenbiao Ding, Jiliang Tang, Songfan Yang, Gale Yan Huang, Zitao Liu

In practice, the crowdsourced labels are usually inconsistent among crowd workers given their diverse expertise and the number of crowdsourced labels is very limited.

Representation Learning

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