Search Results for author: Meng Jiang

Found 48 papers, 21 papers with code

Knowledge-Enriched Natural Language Generation

1 code implementation EMNLP (ACL) 2021 Wenhao Yu, Meng Jiang, Zhiting Hu, Qingyun Wang, Heng Ji, Nazneen Rajani

Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge.

Text Generation

Knowledge-Augmented Methods for Natural Language Processing

no code implementations ACL 2022 Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Lin, Meng Jiang, Wenhao Yu

Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models.

Text Generation

A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality

no code implementations29 Apr 2022 Toby Jia-Jun Li, Yuwen Lu, Jaylexia Clark, Meng Chen, Victor Cox, Meng Jiang, Yang Yang, Tamara Kay, Danielle Wood, Jay Brockman

The AI inequality is caused by (1) the technology divide in who has access to AI technologies in gig work; and (2) the data divide in who owns the data in gig work leads to unfair working conditions, growing pay gap, neglect of workers' diverse preferences, and workers' lack of trust in the platforms.

A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods

no code implementations7 Apr 2022 Zhihan Zhang, Wenhao Yu, Mengxia Yu, Zhichun Guo, Meng Jiang

Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences.

Multi-Task Learning

Graph Data Augmentation for Graph Machine Learning: A Survey

1 code implementation17 Feb 2022 Tong Zhao, Gang Liu, Stephan Günnemann, Meng Jiang

In this paper, we present a comprehensive and systematic survey of graph data augmentation that summarizes the literature in a structured manner.

Data Augmentation

Dict-BERT: Enhancing Language Model Pre-training with Dictionary

no code implementations Findings (ACL) 2022 Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, Meng Jiang

In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word and sentence-level alignment between input text sequence and rare word definitions to enhance language modeling representation with dictionary.

Language Modelling Masked Language Modeling

Multi-Round Parsing-based Multiword Rules for Scientific OpenIE

no code implementations4 Aug 2021 Joseph Kuebler, Lingbo Tong, Meng Jiang

Information extraction (IE) in scientific literature has facilitated many down-stream tasks.

Dependency Parsing

Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations

1 code implementation5 Jun 2021 Qingkai Zeng, Jinfeng Lin, Wenhao Yu, Jane Cleland-Huang, Meng Jiang

Automatic construction of a taxonomy supports many applications in e-commerce, web search, and question answering.

Knowledge Graphs

Cross-Network Learning with Partially Aligned Graph Convolutional Networks

no code implementations3 Jun 2021 Meng Jiang

Graph neural networks have been widely used for learning representations of nodes for many downstream tasks on graph data.

Knowledge Graphs Link Prediction +1

Modeling multi-scale data via a network of networks

no code implementations25 May 2021 Shawn Gu, Meng Jiang, Pietro Hiram Guzzi, Tijana Milenkovic

Prediction of node and graph labels are prominent network science tasks.

Sentence-Permuted Paragraph Generation

1 code implementation EMNLP 2021 Wenhao Yu, Chenguang Zhu, Tong Zhao, Zhichun Guo, Meng Jiang

Generating paragraphs of diverse contents is important in many applications.

TCN: Table Convolutional Network for Web Table Interpretation

no code implementations17 Feb 2021 Daheng Wang, Prashant Shiralkar, Colin Lockard, Binxuan Huang, Xin Luna Dong, Meng Jiang

Existing work linearize table cells and heavily rely on modifying deep language models such as BERT which only captures related cells information in the same table.

Representation Learning Table annotation +1

Few-Shot Graph Learning for Molecular Property Prediction

1 code implementation16 Feb 2021 Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla

The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery.

Drug Discovery Graph Learning +3

Traceability Transformed: Generating moreAccurate Links with Pre-Trained BERT Models

1 code implementation8 Feb 2021 Jinfeng Lin, Yalin Liu, Qingkai Zeng, Meng Jiang, Jane Cleland-Huang

In this study, we propose a novel framework called Trace BERT (T-BERT) to generate trace links between source code and natural language artifacts.

Transfer Learning Software Engineering

Validating Label Consistency in NER Data Annotation

no code implementations EMNLP (Eval4NLP) 2021 Qingkai Zeng, Mengxia Yu, Wenhao Yu, Tianwen Jiang, Meng Jiang

It can be used to validate the label consistency (or catches the inconsistency) in multiple sets of NER data annotation.

Named Entity Recognition NER

FGNAS: FPGA-Aware Graph Neural Architecture Search

no code implementations1 Jan 2021 Qing Lu, Weiwen Jiang, Meng Jiang, Jingtong Hu, Sakyasingha Dasgupta, Yiyu Shi

The success of gragh neural networks (GNNs) in the past years has aroused grow-ing interest and effort in designing best models to handle graph-structured data.

Neural Architecture Search

Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER

no code implementations Findings of the Association for Computational Linguistics 2020 Qingkai Zeng, Wenhao Yu, Mengxia Yu, Tianwen Jiang, Tim Weninger, Meng Jiang

The training process of scientific NER models is commonly performed in two steps: i) Pre-training a language model by self-supervised tasks on huge data and ii) fine-tune training with small labelled data.

Language Modelling NER

Action Sequence Augmentation for Early Graph-based Anomaly Detection

1 code implementation20 Oct 2020 Tong Zhao, Bo Ni, Wenhao Yu, Zhichun Guo, Neil Shah, Meng Jiang

With Eland, anomaly detection performance at an earlier stage is better than non-augmented methods that need significantly more observed data by up to 15% on the Area under the ROC curve.

Anomaly Detection Data Augmentation

Technical Question Answering across Tasks and Domains

1 code implementation NAACL 2021 Wenhao Yu, Lingfei Wu, Yu Deng, Qingkai Zeng, Ruchi Mahindru, Sinem Guven, Meng Jiang

In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains.

Question Answering Reading Comprehension +1

A Survey of Knowledge-Enhanced Text Generation

3 code implementations9 Oct 2020 Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang

To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.

Text Generation

Injecting Entity Types into Entity-Guided Text Generation

2 code implementations EMNLP 2021 Xiangyu Dong, Wenhao Yu, Chenguang Zhu, Meng Jiang

Our model has a multi-step decoder that injects the entity types into the process of entity mention generation.

Text Generation

Federated Dynamic GNN with Secure Aggregation

no code implementations15 Sep 2020 Meng Jiang, Taeho Jung, Ryan Karl, Tong Zhao

Given video data from multiple personal devices or street cameras, can we exploit the structural and dynamic information to learn dynamic representation of objects for applications such as distributed surveillance, without storing data at a central server that leads to a violation of user privacy?

Federated Learning

Specification mining and automated task planning for autonomous robots based on a graph-based spatial temporal logic

no code implementations16 Jul 2020 Zhiyu Liu, Meng Jiang, Hai Lin

For knowledge representation, we use a graph-based spatial temporal logic (GSTL) to capture spatial and temporal information of related skills demonstrated by demo videos.

Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors

1 code implementation11 Jun 2020 Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla

The user embeddings preserve spatial patterns and temporal patterns of a variety of periodicity (e. g., hourly, weekly, and weekday patterns).

Data Augmentation for Graph Neural Networks

1 code implementation11 Jun 2020 Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, Neil Shah

Our work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra-class edges and demote inter-class edges in given graph structure, and our main contribution introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction.

Data Augmentation General Classification +1

A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction

no code implementations WS 2020 Yang Zhou, Tong Zhao, Meng Jiang

Textual patterns (e. g., Country's president Person) are specified and/or generated for extracting factual information from unstructured data.

TAG Text Generation

Crossing Variational Autoencoders for Answer Retrieval

no code implementations ACL 2020 Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, Meng Jiang

Existing methods learned semantic representations with dual encoders or dual variational auto-encoders.

Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning

no code implementations12 Mar 2020 Mandana Saebi, Steven Krieg, Chuxu Zhang, Meng Jiang, Nitesh Chawla

Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fact prediction, and recommender systems.

Knowledge Graphs Question Answering +3

Improving Generalizability of Fake News Detection Methods using Propensity Score Matching

1 code implementation28 Jan 2020 Bo Ni, Zhichun Guo, Jianing Li, Meng Jiang

Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public.

Fake News Detection

Few-Shot Knowledge Graph Completion

1 code implementation26 Nov 2019 Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla

Knowledge graphs (KGs) serve as useful resources for various natural language processing applications.

Knowledge Graph Completion One-Shot Learning

Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method

no code implementations WS 2019 Qingkai Zeng, Mengxia Yu, Wenhao Yu, JinJun Xiong, Yiyu Shi, Meng Jiang

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts.

Face Recognition

Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text

no code implementations IJCNLP 2019 Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh Chawla, Meng Jiang

In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences.


TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering

1 code implementation22 Dec 2018 Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, Jiawei Han

Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion.


PBGen: Partial Binarization of Deconvolution-Based Generators for Edge Intelligence

no code implementations26 Feb 2018 Jinglan Liu, Jiaxin Zhang, Yukun Ding, Xiaowei Xu, Meng Jiang, Yiyu Shi

This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction.


MetaPAD: Meta Pattern Discovery from Massive Text Corpora

no code implementations13 Mar 2017 Meng Jiang, Jingbo Shang, Taylor Cassidy, Xiang Ren, Lance M. Kaplan, Timothy P. Hanratty, Jiawei Han

We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise.

Dependency Parsing

Automated Phrase Mining from Massive Text Corpora

4 code implementations15 Feb 2017 Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R. Voss, Jiawei Han

As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus.


DPPred: An Effective Prediction Framework with Concise Discriminative Patterns

no code implementations31 Oct 2016 Jingbo Shang, Meng Jiang, Wenzhu Tong, Jinfeng Xiao, Jian Peng, Jiawei Han

In the literature, two series of models have been proposed to address prediction problems including classification and regression.

Cannot find the paper you are looking for? You can Submit a new open access paper.