Search Results for author: Danqing Zhang

Found 10 papers, 5 papers with code

MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision

no code implementations EMNLP 2021 Zheng Li, Danqing Zhang, Tianyu Cao, Ying WEI, Yiwei Song, Bing Yin

In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages.

Meta-Learning

Condensing Graphs via One-Step Gradient Matching

3 code implementations15 Jun 2022 Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin

However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization.

Dataset Condensation

RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph

no code implementations12 Feb 2022 Ruijie Wang, Zheng Li, Danqing Zhang, Qingyu Yin, Tong Zhao, Bing Yin, Tarek Abdelzaher

And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction.

Product Recommendation Retrieval

QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction

no code implementations19 Aug 2021 Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Hanqing Lu, Yiwei Song, Bing Yin, Tuo Zhao, Qiang Yang

We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms.

Attribute Attribute Value Extraction +3

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

1 code implementation ACL 2021 Haoming Jiang, Danqing Zhang, Tianyu Cao, Bing Yin, Tuo Zhao

Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data.

named-entity-recognition Named Entity Recognition +1

On Data Augmentation for Extreme Multi-label Classification

no code implementations22 Sep 2020 Danqing Zhang, Tao Li, Haiyang Zhang, Bing Yin

Our contributions are two-factored: (1) we introduce a new state-of-the-art classifier that uses label attention with RoBERTa and combine it with our augmentation framework for further improvement; (2) we present a broad study on how effective are different augmentation methods in the XMC task.

Classification Data Augmentation +2

Predicting Driver Attention in Critical Situations

2 code implementations17 Nov 2017 Ye Xia, Danqing Zhang, Jinkyu Kim, Ken Nakayama, Karl Zipser, David Whitney

Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol---tracking eye movements during driving.

Autonomous Driving Driver Attention Monitoring

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