activity
20182022
most citedDOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction

13 citations · 19 across the 6 of their papers we have counts for

collaborators

10 papers

cs.CL20221 cited

CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking

Haoning Zhang, Junwei Bao, Haipeng Sun +3

Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data. Existing few-shot methods mainly transfer knowledge learned from…

cs.CL2021

GEM: A General Evaluation Benchmark for Multimodal Tasks

Lin Su, Nan Duan, Edward Cui +7

In this paper, we present GEM as a General Evaluation benchmark for Multimodal tasks. Different from existing datasets such as GLUE, SuperGLUE, XGLUE and XTREME that mainly focus o…

cs.CV2021

CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

Huaishao Luo, Lei Ji, Ming Zhong +4

Video-text retrieval plays an essential role in multi-modal research and has been widely used in many real-world web applications. The CLIP (Contrastive Language-Image Pre-training…

cs.CL2020

MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension

Huaishao Luo, Yu Shi, Ming Gong +2

Span extraction is an essential problem in machine reading comprehension. Most of the existing algorithms predict the start and end positions of an answer span in the given corresp…

cs.CL20204 cited

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

Huaishao Luo, Lei Ji, Tianrui Li +2

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks…

cs.SI20201 cited

ReAD: A Regional Anomaly Detection Framework Based on Dynamic Partition

Huaishao Luo, Chuishi Meng, Bowen Wu +3

The detection of the abnormal area from urban data is a significant research problem. However, to the best of our knowledge, previous methods designed on spatio-temporal anomalies…