Search Results for author:
Found 84 papers, 38 papers with code
HypMix: Hyperbolic Interpolative Data Augmentation
Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities.
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs.
DMix: Adaptive Distance-aware Interpolative Mixup
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.
InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting
A channel identifier, a global mixing module and a self-contextual attention module are devised in InjectTST.
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN.
Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language
Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations.
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era.
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps.
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge
We hope this task and dataset can promote further research on TOD and subjective content understanding.
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
Deep Graph Neural Networks via Flexible Subgraph Aggregation
Based on this, we propose a sampling-based node-level residual module (SNR) that can achieve a more flexible utilization of different hops of subgraph aggregation by introducing node-level parameters sampled from a learnable distribution.
KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks
Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes.
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
This work focuses on in-context data augmentation for intent detection.
Ranked #1 on
Intent Detection
on BANKING77 10-shot
Using In-Context Learning to Improve Dialogue Safety
For instance, using automatic evaluation, we find our best fine-tuned baseline only generates safe responses to unsafe dialogue contexts from DiaSafety 4. 04% more than our approach.
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs.
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i. e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors.
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines
These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer's expectations and intent.
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning
Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning.
A Systematic Evaluation of Response Selection for Open Domain Dialogue
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
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.
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
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.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
TeKo: Text-Rich Graph Neural Networks with External Knowledge
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
Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging.
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
This paper revisits the approach from a matrix approximation perspective, and identifies two issues in the existing layer-wise sampling methods: suboptimal sampling probabilities and estimation biases induced by sampling without replacement.
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
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.
TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature
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.
On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets
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.
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention
The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks.
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
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.
Towards Textual Out-of-Domain Detection without In-Domain Labels
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.
TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding
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.
Graph Neural Networks for Graphs with Heterophily: A Survey
In the end, we point out the potential directions to advance and stimulate more future research and applications on heterophilic graph learning with GNNs.
Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily
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
Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules.
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
In this review, we focus on the interpretability of the DL models in healthcare.
Universal Graph Convolutional Networks
So can we reasonably utilize these segmentation rules to design a universal propagation mechanism independent of the network structural assumption?
Leveraging the Graph Structure of Neural Network Training Dynamics
Understanding the training dynamics of deep neural networks (DNNs) is important as it can lead to improved training efficiency and task performance.
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation
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.
Revisiting Layer-wise Sampling in Fast Training for Graph Convolutional Networks
To accelerate the training of graph convolutional networks (GCN), many sampling-based methods have been developed for approximating the embedding aggregation.
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets.
Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems
Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs.
Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling
Most prior work on task-oriented dialogue systems are restricted to limited coverage of domain APIs.
A Comprehensive Survey on Community Detection with Deep Learning
A community reveals the features and connections of its members that are different from those in other communities in a network.
Adversarial Contrastive Pre-training for Protein Sequences
Recent developments in Natural Language Processing (NLP) demonstrate that large-scale, self-supervised pre-training can be extremely beneficial for downstream tasks.
Towards Understanding and Evaluating Structural Node Embeddings
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
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.
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
Augmenting NLP models using Latent Feature Interpolations
Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution.
Deep Learning for Text Style Transfer: A Survey
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.
BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks
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
In this work, we adopt GNN for a classic but challenging nonlinear regression problem, namely the network localization.
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
Relevance has significant impact on user experience and business profit for e-commerce search platform.
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community.
From Static to Dynamic Node Embeddings
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.
Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis
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
Aspect-Based Sentiment Analysis (ABSA)
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards
The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning.
GCN for HIN via Implicit Utilization of Attention and Meta-paths
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
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.
Learning Continuous-Time Dynamics by Stochastic Differential Networks
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.
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness.
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap
In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets.
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure.
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on new domains with no supervised data.
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
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.
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
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.
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
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.
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition
We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).
DATNet: Dual Adversarial Transfer for Low-resource Named Entity Recognition
We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).
Low Resource Named Entity Recognition
named-entity-recognition
+2
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
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
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.
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content.
Latent Network Summarization: Bridging Network Embedding and Summarization
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
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.
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts
Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear.
Ranked #1 on
Sentence Classification
on PubMed 20k RCT
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention
They ignore that one discusses diverse topics when dynamically interacting with different people.
Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning
In this paper, we propose a novel neural Tensor network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition.
PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks
Successful evidence-based medicine (EBM) applications rely on answering clinical questions by analyzing large medical literature databases.
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network
SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types.
High-throughput, high-resolution registration-free generated adversarial network microscopy
We combine generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV).
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
Our proposed system consists of two subsystems and one regression model for predicting STS scores.
Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter
Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues.
