Search Results for author: Pinghui Wang

Found 15 papers, 10 papers with code

Robust Visual Question Answering: Datasets, Methods, and Future Challenges

no code implementations21 Jul 2023 Jie Ma, Pinghui Wang, Dechen Kong, Zewei Wang, Jun Liu, Hongbin Pei, Junzhou Zhao

Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives.

Question Answering Visual Question Answering

MERGE: Fast Private Text Generation

1 code implementation25 May 2023 Zi Liang, Pinghui Wang, Ruofei Zhang, Lifeng Xing, Nuo Xu, Shuo Zhang

Recent years have seen increasing concerns about the private inference of NLP services and Transformer models.

Code Completion Privacy Preserving +1

Healing Unsafe Dialogue Responses with Weak Supervision Signals

1 code implementation25 May 2023 Zi Liang, Pinghui Wang, Ruofei Zhang, Shuo Zhang, Xiaofan Ye Yi Huang, Junlan Feng

Recent years have seen increasing concerns about the unsafe response generation of large-scale dialogue systems, where agents will learn offensive or biased behaviors from the real-world corpus.

Pseudo Label Response Generation

Adaptive loose optimization for robust question answering

1 code implementation6 May 2023 Jie Ma, Pinghui Wang, Zewei Wang, Dechen Kong, Min Hu, Ting Han, Jun Liu

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering).

Extractive Question-Answering Machine Reading Comprehension +2

Multi-Action Dialog Policy Learning from Logged User Feedback

no code implementations27 Feb 2023 Shuo Zhang, Junzhou Zhao, Pinghui Wang, Tianxiang Wang, Zi Liang, Jing Tao, Yi Huang, Junlan Feng

To cope with this problem, we explore to improve multi-action dialog policy learning with explicit and implicit turn-level user feedback received for historical predictions (i. e., logged user feedback) that are cost-efficient to collect and faithful to real-world scenarios.

Fast Gumbel-Max Sketch and its Applications

no code implementations10 Feb 2023 Yuanming Zhang, Pinghui Wang, Yiyan Qi, Kuankuan Cheng, Junzhou Zhao, Guangjian Tian, Xiaohong Guan

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval.

Information Retrieval Retrieval

Federated Learning over Coupled Graphs

no code implementations26 Jan 2023 Runze Lei, Pinghui Wang, Junzhou Zhao, Lin Lan, Jing Tao, Chao Deng, Junlan Feng, Xidian Wang, Xiaohong Guan

In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.

Federated Learning Node Classification

"Think Before You Speak": Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs

1 code implementation25 Apr 2022 Shuo Zhang, Junzhou Zhao, Pinghui Wang, Yu Li, Yi Huang, Junlan Feng

Multi-action dialog policy (MADP), which generates multiple atomic dialog actions per turn, has been widely applied in task-oriented dialog systems to provide expressive and efficient system responses.

Multi-Task Learning

Learning to Check Contract Inconsistencies

1 code implementation15 Dec 2020 Shuo Zhang, Junzhou Zhao, Pinghui Wang, Nuo Xu, Yang Yang, Yiting Liu, Yi Huang, Junlan Feng

This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract.

XTQA: Span-Level Explanations of the Textbook Question Answering

1 code implementation25 Nov 2020 Jie Ma, Qi Chai, Jun Liu, Qingyu Yin, Pinghui Wang, Qinghua Zheng

Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams.

Question Answering

Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding

1 code implementation NeurIPS 2020 Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, Xiaohong Guan

We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.

General Classification Graph structure learning +3

Distinguish Confusing Law Articles for Legal Judgment Prediction

1 code implementation ACL 2020 Nuo Xu, Pinghui Wang, Long Chen, Li Pan, Xiaoyan Wang, Junzhou Zhao

Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public.

Fast Generating A Large Number of Gumbel-Max Variables

no code implementations2 Feb 2020 Yiyan Qi, Pinghui Wang, Yuanming Zhang, Junzhou Zhao, Guangjian Tian, Xiaohong Guan

Instead of computing $k$ independent Gumbel random variables directly, we find that there exists a technique to generate these variables in descending order.

Graph Embedding Information Retrieval +1

MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

2 code implementations23 May 2019 Nuo Xu, Pinghui Wang, Long Chen, Jing Tao, Junzhou Zhao

To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs.

Meta Reinforcement Learning with Task Embedding and Shared Policy

2 code implementations16 May 2019 Lin Lan, Zhenguo Li, Xiaohong Guan, Pinghui Wang

Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.

Meta-Learning Meta Reinforcement Learning +2

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