Search Results for author: Di Jin

Found 68 papers, 27 papers with code

DMix: Adaptive Distance-aware Interpolative Mixup

1 code implementation ACL 2022 Ramit Sawhney, Megh Thakkar, Shrey Pandit, Ritesh Soun, Di Jin, Diyi Yang, Lucie Flek

Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities. We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space.

Data Augmentation Sentence Classification

HypMix: Hyperbolic Interpolative Data Augmentation

1 code implementation EMNLP 2021 Ramit Sawhney, Megh Thakkar, Shivam Agarwal, Di Jin, Diyi Yang, Lucie Flek

Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities.

Adversarial Robustness Data Augmentation

Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs

no code implementations NAACL 2022 Sha Li, Mahdi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tur

In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs.

Multi-Task Learning Response Generation

A Systematic Evaluation of Response Selection for Open Domain Dialogue

no code implementations8 Aug 2022 Behnam Hedayatnia, Di Jin, Yang Liu, Dilek Hakkani-Tur

In this work, we curated a dataset where responses from multiple response generators produced for the same dialog context are manually annotated as appropriate (positive) and inappropriate (negative).

Improving Bot Response Contradiction Detection via Utterance Rewriting

no code implementations25 Jul 2022 Di Jin, Sijia Liu, Yang Liu, Dilek Hakkani-Tur

Previous work has treated contradiction detection in bot responses as a task similar to natural language inference, e. g., detect the contradiction between a pair of bot utterances.

Natural Language Inference

RAW-GNN: RAndom Walk Aggregation based Graph Neural Network

no code implementations28 Jun 2022 Di Jin, Rui Wang, Meng Ge, Dongxiao He, Xiang Li, Wei Lin, Weixiong Zhang

Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent.

Representation Learning

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Text Generation

TeKo: Text-Rich Graph Neural Networks with External Knowledge

no code implementations15 Jun 2022 Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao, Jiawei Han, Lingfei Wu

Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).

Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs

no code implementations15 Jun 2022 Sha Li, Mahdi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tur

Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging.

Multi-Task Learning Response Generation

Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks

no code implementations1 Jun 2022 Yifan Chen, Tianning Xu, Dilek Hakkani-Tur, Di Jin, Yun Yang, Ruoqing Zhu

We identify two issues in the existing layer-wise sampling methods: sub-optimal sampling probabilities and the approximation bias induced by sampling without replacement.

CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning

no code implementations30 May 2022 Di Jin, Luzhi Wang, Yizhen Zheng, Xiang Li, Fei Jiang, Wei Lin, Shirui Pan

As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score.

Collaborative Filtering Graph Classification +4

TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature

no code implementations25 May 2022 Cuiying Huo, Di Jin, Chundong Liang, Dongxiao He, Tie Qiu, Lingfei Wu

In this work, we propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation.

Recommendation Systems

On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets

no code implementations insights (ACL) 2022 Hyounghun Kim, Aishwarya Padmakumar, Di Jin, Mohit Bansal, Dilek Hakkani-Tur

Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes.

Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning

no code implementations30 Apr 2022 Di Jin, Cuiying Huo, Jianwu Dang, Peican Zhu, Weixiong Zhang, Witold Pedrycz, Lingfei Wu

However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e. g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies.

Contrastive Learning

Towards Textual Out-of-Domain Detection without In-Domain Labels

no code implementations22 Mar 2022 Di Jin, Shuyang Gao, Seokhwan Kim, Yang Liu, Dilek Hakkani-Tur

In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions.

Contrastive Learning Intent Classification +3

TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding

no code implementations17 Mar 2022 Ruiteng Zhang, Jianguo Wei, Xugang Lu, Wenhuan Lu, Di Jin, Junhai Xu, Lin Zhang, Yantao Ji, Jianwu Dang

Therefore, in the most current state-of-the-art network architectures, only a few branches corresponding to a limited number of temporal scales could be designed for speaker embeddings.

Speaker Verification

Graph Neural Networks for Graphs with Heterophily: A Survey

no code implementations14 Feb 2022 Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, Philip S. Yu

Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriads of graph analytic tasks and applications.

Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily

no code implementations27 Dec 2021 Tao Wang, Rui Wang, Di Jin, Dongxiao He, Yuxiao Huang

To address this problem, in this paper we design a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs.

Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences

1 code implementation NAACL 2022 Yifan Chen, Qi Zeng, Dilek Hakkani-Tur, Di Jin, Heng Ji, Yun Yang

Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules.

Universal Graph Convolutional Networks

1 code implementation NeurIPS 2021 Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang, Dongxiao He, Jiawei Han

So can we reasonably utilize these segmentation rules to design a universal propagation mechanism independent of the network structural assumption?

Convolutional Neural Network Dynamics: A Graph Perspective

no code implementations9 Nov 2021 Fatemeh Vahedian, Ruiyu Li, Puja Trivedi, Di Jin, Danai Koutra

Specifically, we propose (1) representing the neural network learning process as a time-evolving graph (i. e., a series of static graph snapshots over epochs), (2) capturing the structural changes of the NN during the training phase in a simple temporal summary, and (3) leveraging the structural summary to predict the accuracy of the underlying NN in a classification or regression task.

Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation

1 code implementation27 Oct 2021 Di Jin, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Danai Koutra

AdaMEL models the attribute importance that is used to match entities through an attribute-level self-attention mechanism, and leverages the massive unlabeled data from new data sources through domain adaptation to make it generic and data-source agnostic.

Domain Adaptation

Revisiting Layer-wise Sampling in Fast Training for Graph Convolutional Networks

no code implementations29 Sep 2021 Yifan Chen, Tianning Xu, Dilek Hakkani-Tur, Di Jin, Yun Yang, Ruoqing Zhu

To accelerate the training of graph convolutional networks (GCN), many sampling-based methods have been developed for approximating the embedding aggregation.

Adversarial Contrastive Pre-training for Protein Sequences

no code implementations31 Jan 2021 Matthew B. A. McDermott, Brendan Yap, Harry Hsu, Di Jin, Peter Szolovits

Recent developments in Natural Language Processing (NLP) demonstrate that large-scale, self-supervised pre-training can be extremely beneficial for downstream tasks.

Language Modelling Natural Language Processing

Towards Understanding and Evaluating Structural Node Embeddings

1 code implementation14 Jan 2021 Junchen Jin, Mark Heimann, Di Jin, Danai Koutra

While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences, a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles--i. e., similar functions, ties or interactions with nodes in other positions--irrespective of their distance or reachability in the network.

Network Embedding Social and Information Networks

Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

no code implementations13 Jan 2021 Ziyang Liu, Zhaomeng Cheng, Yunjiang Jiang, Yue Shang, Wei Xiong, Sulong Xu, Bo Long, Di Jin

We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.

Network Embedding

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

no code implementations3 Jan 2021 Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang

We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

Community Detection

Deep Learning for Text Style Transfer: A Survey

2 code implementations CL (ACL) 2022 Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others.

Natural Language Processing Style Transfer +2

BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks

no code implementations23 Oct 2020 Di Jin, Xiangchen Song, Zhizhi Yu, Ziyang Liu, Heling Zhang, Zhaomeng Cheng, Jiawei Han

We propose BiTe-GCN, a novel GCN architecture with bidirectional convolution of both topology and features on text-rich networks to solve these limitations.

Graph Neural Network for Large-Scale Network Localization

1 code implementation22 Oct 2020 Wenzhong Yan, Di Jin, Zhidi Lin, Feng Yin

In this work, we adopt GNN for a classic but challenging nonlinear regression problem, namely the network localization.

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

1 code implementation28 Sep 2020 Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, Peter Szolovits

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community.

Multiple-choice Natural Language Processing +2

From Static to Dynamic Node Embeddings

no code implementations21 Sep 2020 Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra

While previous work on dynamic modeling and embedding has focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale (e. g., 1 month), we propose the notion of an $\epsilon$-graph time-series that uses a fixed number of edges for each graph, and show its superiority over the time-scale representation used in previous work.

Time Series

Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis

1 code implementation EMNLP 2020 Xiaoyu Xing, Zhijing Jin, Di Jin, Bingning Wang, Qi Zhang, Xuanjing Huang

Based on the SemEval 2014 dataset, we construct the Aspect Robustness Test Set (ARTS) as a comprehensive probe of the aspect robustness of ABSA models.

Aspect-Based Sentiment Analysis

GCN for HIN via Implicit Utilization of Attention and Meta-paths

no code implementations6 Jul 2020 Di Jin, Zhizhi Yu, Dongxiao He, Carl Yang, Philip S. Yu, Jiawei Han

Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors.

Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering

no code implementations ACL 2020 Ming Yan, Hao Zhang, Di Jin, Joey Tianyi Zhou

Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations.

Arithmetic Logical Reasoning +5

Learning Continuous-Time Dynamics by Stochastic Differential Networks

no code implementations11 Jun 2020 Yingru Liu, Yucheng Xing, Xuewen Yang, Xin Wang, Jing Shi, Di Jin, Zhaoyue Chen

Learning continuous-time stochastic dynamics is a fundamental and essential problem in modeling sporadic time series, whose observations are irregular and sparse in both time and dimension.

Time Series

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

2 code implementations EMNLP 2020 John X. Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, Yanjun Qi

TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness.

Adversarial Attack Adversarial Text +4

Hooks in the Headline: Learning to Generate Headlines with Controlled Styles

1 code implementation ACL 2020 Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, Peter Szolovits

Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure.

Headline generation

A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation

1 code implementation22 Jan 2020 Di Jin, Zhijing Jin, Joey Tianyi Zhou, Peter Szolovits

State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on new domains with no supervised data.

Language Modelling Machine Translation +2

MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension

2 code implementations1 Oct 2019 Di Jin, Shuyang Gao, Jiun-Yu Kao, Tagyoung Chung, Dilek Hakkani-Tur

Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language.

Arithmetic Logical Reasoning +4

On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications

no code implementations22 Aug 2019 Ryan A. Rossi, Di Jin, Sungchul Kim, Nesreen K. Ahmed, Danai Koutra, John Boaz Lee

Unfortunately, recent work has sometimes confused the notion of structural roles and communities (based on proximity) leading to misleading or incorrect claims about the capabilities of network embedding methods.

Misconceptions Network Embedding

Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

7 code implementations27 Jul 2019 Di Jin, Zhijing Jin, Joey Tianyi Zhou, Peter Szolovits

Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models.

Adversarial Text General Classification +2

node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching

1 code implementation18 Apr 2019 Di Jin, Mark Heimann, Ryan Rossi, Danai Koutra

Identity stitching, the task of identifying and matching various online references (e. g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalization and recommendations.

Publicly Available Clinical BERT Embeddings

2 code implementations WS 2019 Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, Matthew B. A. McDermott

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months.

De-identification Natural Language Processing

IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation

3 code implementations IJCNLP 2019 Zhijing Jin, Di Jin, Jonas Mueller, Nicholas Matthews, Enrico Santus

Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content.

Style Transfer Text Attribute Transfer +2

Latent Network Summarization: Bridging Network Embedding and Summarization

1 code implementation11 Nov 2018 Di Jin, Ryan Rossi, Danai Koutra, Eunyee Koh, Sungchul Kim, Anup Rao

Motivated by the computational and storage challenges that dense embeddings pose, we introduce the problem of latent network summarization that aims to learn a compact, latent representation of the graph structure with dimensionality that is independent of the input graph size (i. e., #nodes and #edges), while retaining the ability to derive node representations on the fly.

Social and Information Networks

Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks

1 code implementation30 Oct 2018 Di Jin, Peter Szolovits

One is the PubMed-PICO dataset, where our best results outperform the previous best by 5. 5%, 7. 9%, and 5. 8% for P, I, and O elements in terms of F1 score, respectively.

feature selection PICO +1

Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts

1 code implementation EMNLP 2018 Di Jin, Peter Szolovits

Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear.

Classification General Classification +2

Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning

no code implementations COLING 2018 Fengyu Guo, Ruifang He, Di Jin, Jianwu Dang, Longbiao Wang, Xiangang Li

In this paper, we propose a novel neural Tensor network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition.

Sparse Learning Text Summarization

PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks

2 code implementations WS 2018 Di Jin, Peter Szolovits

Successful evidence-based medicine (EBM) applications rely on answering clinical questions by analyzing large medical literature databases.

Decision Making General Classification +1

High-throughput, high-resolution registration-free generated adversarial network microscopy

1 code implementation7 Jan 2018 Hao Zhang, Xinlin Xie, Chunyu Fang, Yicong Yang, Di Jin, Peng Fei

We combine generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV).

Image Registration Image Super-Resolution +1

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