38 citations · 57 across the 7 of their papers we have counts for
7 papers
Language-Assisted Deep Learning for Autistic Behaviors Recognition
Andong Deng, Taojiannan Yang, Chen Chen +3
Correctly recognizing the behaviors of children with Autism Spectrum Disorder (ASD) is of vital importance for the diagnosis of Autism and timely early intervention. However, the o…
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
Linhan Zhang, Qian Chen, Wen Wang +5
Keyphrase extraction (KPE) automatically extracts phrases in a document that provide a concise summary of the core content, which benefits downstream information retrieval and NLP…
Towards Making Deep Learning-based Vulnerability Detectors Robust
Zhen Li, Jing Tang, Deqing Zou +5
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention. In particular, deep learning-based vulnerability detector…
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Mengcheng Lan, Shuliang Ning, Yanran Li +4
Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but complet…
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation
Qinglin Zhang, Qian Chen, Yali Li +2
Transcripts generated by automatic speech recognition (ASR) systems for spoken documents lack structural annotations such as paragraphs, significantly reducing their readability. A…
Several Experiments on Investigating Pretraining and Knowledge-Enhanced Models for Natural Language Inference
Tianda Li, Xiaodan Zhu, Quan Liu +3
Natural language inference (NLI) is among the most challenging tasks in natural language understanding. Recent work on unsupervised pretraining that leverages unsupervised signals…
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
Junbei Zhang, Xiaodan Zhu, Qian Chen +3
The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach…