Search Results for author:
Found 127 papers, 58 papers with code
Progressive Self-Training with Discriminator for Aspect Term Extraction
In this paper, we use two means to alleviate the noise in the pseudo-labels.
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis
This essentially allows the framework to understand the appropriate graph structures for learning intricate relations among different modalities.
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge
To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge.
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training
Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student.
Ranked #5 on
Task-Oriented Dialogue Systems
on KVRET
Dependency-aware Prototype Learning for Few-shot Relation Classification
This paper presents a novel dependency-aware prototype learning (DAPL) method for few-shot relation classification.
Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing
In this paper, we propose a novel Spurious Correlation reduction method to improve the robustness of the neural ANswer selection models (SCAN) from the sample and feature perspectives by removing the feature dependencies and language biases in answer selection.
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection
In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning.
A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems
Most existing neural network based task-oriented dialog systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability.
Ranked #9 on
Task-Oriented Dialogue Systems
on KVRET
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network
Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance.
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing
Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET).
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph
In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage.
Ranked #1 on
Argument Pair Extraction (APE)
on RR
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models
Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these intricate contexts.
VDialogUE: A Unified Evaluation Benchmark for Visually-grounded Dialogue
Visually-grounded dialog systems, which integrate multiple modes of communication such as text and visual inputs, have become an increasingly popular area of investigation.
MIRA: Cracking Black-box Watermarking on Deep Neural Networks via Model Inversion-based Removal Attacks
In this paper, we propose a novel Model Inversion-based Removal Attack (\textsc{Mira}), which is watermark-agnostic and effective against most of mainstream black-box DNN watermarking schemes.
DiffusionTrack: Diffusion Model For Multi-Object Tracking
In inference, the model refines a set of paired randomly generated boxes to the detection and tracking results in a flexible one-step or multi-step denoising diffusion process.
FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings
Logo embedding plays a crucial role in various e-commerce applications by facilitating image retrieval or recognition, such as intellectual property protection and product search.
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations
We present a new category of physics-informed neural networks called physics informed variational embedding generative adversarial network (PI-VEGAN), that effectively tackles the forward, inverse, and mixed problems of stochastic differential equations.
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments.
A serial dual-channel library occupancy detection system based on Faster RCNN
The phenomenon of seat occupancy in university libraries is a prevalent issue.
Valley: Video Assistant with Large Language model Enhanced abilitY
Specifically, our proposed Valley model is designed with a simple projection module that bridges video, image, and language modalities, and is further unified with a multi-lingual LLM.
A Comprehensive Survey on Deep Learning for Relation Extraction: Recent Advances and New Frontiers
This survey is expected to facilitate researchers' collaborative efforts to tackle the challenges of real-life RE systems.
Topic-Guided Self-Introduction Generation for Social Media Users
To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests.
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
It utilizes a combination of several fundamental experts to accommodate multiple dialogue-related tasks and can be pre-trained using limited dialogue and extensive non-dialogue multi-modal data.
Ranked #1 on
Response Generation
on SIMMC2.0
Iterative Forward Tuning Boosts In-context Learning in Language Models
In this paper, we propose an effective and efficient two-stage framework to boost ICL in LLMs by exploiting a dual form between Transformer attention and gradient descent-based optimization.
Self-Distillation with Meta Learning for Knowledge Graph Completion
In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the longtail samples.
Ranked #3 on
Link Prediction
on FB15k-237
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
In this paper, we propose Speech-text dialog Pre-training for spoken dialog understanding with ExpliCiT cRoss-Modal Alignment (SPECTRA), which is the first-ever speech-text dialog pre-training model.
Ranked #1 on
Multimodal Sentiment Analysis
on MOSI
Emotion Recognition in Conversation
Multimodal Intent Recognition
+1
Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation
Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks.
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment
Entity alignment(EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs(KGs), which aims to discover entities referring to the same real-world object from different KGs.
OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment
In addition, external resources are usually unavailable in some scenarios especially cross-lingual and cross-domain applications, which reflects the little scalability of these methods.
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention
In this paper, we propose a novel multi-agent reinforcement learning algorithm, Partially Observable Mean Field Multi-Agent Reinforcement Learning based on Graph--Attention (GAMFQ) to remedy this flaw.
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification.
Exorcising ''Wraith'': Protecting LiDAR-based Object Detector in Automated Driving System from Appearing Attacks
In this paper, we propose a novel plug-and-play defensive module which works by side of a trained LiDAR-based object detector to eliminate forged obstacles where a major proportion of local parts have low objectness, i. e., to what degree it belongs to a real object.
Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation
Copyright protection for deep neural networks (DNNs) is an urgent need for AI corporations.
Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning
Concretely, we employ the Variational Information Bottleneck (VIB) principle to learn an informative and compressed network (self-pruned network) from the original network, which discards the superfluous patterns or spurious correlations between input features and prediction labels.
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning.
Ranked #1 on
Semantic Parsing
on WikiTableQuestions
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling
Recently, deep neural networks have shown great success on 3D point cloud classification tasks, which simultaneously raises the concern of adversarial attacks that cause severe damage to real-world applications.
Rethinking Feature-Based Knowledge Distillation for Face Recognition
By constraining the teacher's search space with reverse distillation, we narrow the intrinsic gap and unleash the potential of feature-only distillation.
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning
First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well.
Self-consistent Reasoning For Solving Math Word Problems
Specifically, we firstly obtain a sub-network by pruning a roberta2tree model, for the sake to use the gap on output distribution between the original roberta2tree model and the pruned sub-network to expose spurious correlative samples.
Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems
Although Physics-Informed Neural Networks (PINNs) have been successfully applied in a wide variety of science and engineering fields, they can fail to accurately predict the underlying solution in slightly challenging convection-diffusion-reaction problems.
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity.
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.
It Takes Two: Masked Appearance-Motion Modeling for Self-supervised Video Transformer Pre-training
In order to guide the encoder to fully excavate spatial-temporal features, two separate decoders are used for two pretext tasks of disentangled appearance and motion prediction.
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations.
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding
Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks.
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other.
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query.
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval
We introduce dynamic dictionaries for both modalities to enlarge the scale of image-text pairs, and diversity-sensitiveness is achieved by adaptive negative pair weighting.
A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling
To address this design flaw, we propose a simple yet effective security patch for KF-based MOT, the core of which is an adaptive strategy to balance the focus of KF on observations and predictions according to the anomaly index of the observation-prediction deviation, and has certified effectiveness against a generalized hijacking attack model.
A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation
To solve this problem, we propose a Multi-Modality Ovarian Tumor Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170 contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise annotations.
2nd Place Solution to Google Landmark Retrieval 2020
Using this method, we got a public score of 0. 40176 and a private score of 0. 36278 and achieved 2nd place in the Google Landmark Retrieval Competition 2020.
DALG: Deep Attentive Local and Global Modeling for Image Retrieval
Deeply learned representations have achieved superior image retrieval performance in a retrieve-then-rerank manner.
Matryoshka: Stealing Functionality of Private ML Data by Hiding Models in Model
In this paper, we present a novel insider attack called Matryoshka, which employs an irrelevant scheduled-to-publish DNN model as a carrier model for covert transmission of multiple secret models which memorize the functionality of private ML data stored in local data centers.
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i. e., properly recognizing mentions of unseen columns or tables when generating SQLs.
BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
Empirical results demonstrate that our PlusTAD is very efficient and significantly outperforms the previous methods on the datasets of THUMOS14 and FineAction.
Ranked #1 on
Temporal Action Localization
on THUMOS14
Cracking White-box DNN Watermarks via Invariant Neuron Transforms
On $9$ state-of-the-art white-box watermarking schemes and a broad set of industry-level DNN architectures, our attack for the first time reduces the embedded identity message in the protected models to be almost random.
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
The adversarial robustness of a neural network mainly relies on two factors, one is the feature representation capacity of the network, and the other is its resistance ability to perturbations.
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions
Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives.
A Survey of Natural Language Generation
This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology.
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems.
Ranked #1 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
Linking-Enhanced Pre-Training for Table Semantic Parsing
Recently pre-training models have significantly improved the performance of various NLP tasks by leveraging large-scale text corpora to improve the contextual representation ability of the neural network.
A Chinese Multi-type Complex Questions Answering Dataset over Wikidata
We finally analyze the performance of SOTA KBQA models on this dataset and identify the challenges facing Chinese KBQA.
Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning
Recovering binary programs' call graphs is crucial for inter-procedural analysis tasks and applications based on them. transfer One of the core challenges is recognizing targets of indirect calls (i. e., indirect callees).
Enhancing Top-N Item Recommendations by Peer Collaboration
In this paper, we plan to exploit such redundancy phenomena to improve the performance of RS.
DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
Components orthogonal to the global image representation are then extracted from the local information.
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews
In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction.
Missingness Augmentation: A General Approach for Improving Generative Imputation Models
However, data augmentation, as a simple yet effective method, has not received enough attention in this area.
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking
This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system.
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search
To realize such a goal, we propose AdaRec, a knowledge distillation (KD) framework which compresses knowledge of a teacher model into a student model adaptively according to its recommendation scene by using differentiable Neural Architecture Search (NAS).
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification
In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization.
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain.
A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems
Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability.
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks
Neural networks are susceptible to artificially designed adversarial perturbations.
Ranked #1 on
Adversarial Attack
on CIFAR-10
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning
Finally, we integrate the imaginary concepts and relational knowledge to generate human-like story based on the original semantics of images.
Ranked #2 on
Visual Storytelling
on VIST
Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge
Answer selection, which is involved in many natural language processing applications such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge.
Adversarial Momentum-Contrastive Pre-Training
Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which will bring heavy computational overhead on platforms with limited resources.
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets.
The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement
In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style.
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems
To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager.
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning
In this paper, we propose an Interactive key-value Memory- augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions.
PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data
Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results.
Applications of Triggered Scaler Module for Accelerator Timing
The second one, an application for the abort signal of a machine protection system (MPS), showed that the module is capable of visualizing signals based on a relationship with the accelerator cycle.
Accelerator Physics
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis
Among existing privacy attacks on the gradient of neural networks, \emph{data reconstruction attack}, which reverse engineers the training batch from the gradient, poses a severe threat on the private training data.
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's Distance
In this way, our model can learn from different teacher layers adaptively for various NLP tasks.
Improve Adversarial Robustness via Weight Penalization on Classification Layer
It is well-known that deep neural networks are vulnerable to adversarial attacks.
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge
Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags.
Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction
Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text.
Ranked #2 on
Emotion-Cause Pair Extraction
on ECPE-FanSplit
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
One of the difficulties of this competition is how to use unlabeled data.
Ranked #226 on
Image Classification
on ImageNet
A Generic Network Compression Framework for Sequential Recommender Systems
Current state-of-the-art sequential recommender models are typically based on a sandwich-structured deep neural network, where one or more middle (hidden) layers are placed between the input embedding layer and output softmax layer.
Convolutional Hierarchical Attention Network for Query-Focused Video Summarization
This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary.
Leveraging Long and Short-Term Information in Content-Aware Movie Recommendation via Adversarial Training
Movie recommendation systems provide users with ranked lists of movies based on individual’s preferences and constraints.
Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network
In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown.
Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation.
A Fast deflation Method for Sparse Principal Component Analysis via Subspace Projections
The implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work.
Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
Community question answering (CQA) gains increasing popularity in both academy and industry recently.
Reading Like HER: Human Reading Inspired Extractive Summarization
In this work, we re-examine the problem of extractive text summarization for long documents.
Ranked #8 on
Extractive Text Summarization
on CNN / Daily Mail
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on MAMS
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis
Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis.
Ranked #2 on
Emotion Cause Extraction
on ECE
Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing
Multi-Task Learning (MTL) aims at boosting the overall performance of each individual task by leveraging useful information contained in multiple related tasks.
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes.
Ranked #1 on
Text Classification
on RCV1
(P@1 metric)
NAIRS: A Neural Attentive Interpretable Recommendation System
In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS.
GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks
Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks.
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization, b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given.
Dial2Desc: End-to-end Dialogue Description Generation
We first propose a new task named Dialogue Description (Dial2Desc).
Textually Guided Ranking Network for Attentional Image Retweet Modeling
In this paper, we study the problem of image retweet prediction in social media, which predicts the image sharing behavior that the user reposts the image tweets from their followees.
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation.
Databases
Cooperative Denoising for Distantly Supervised Relation Extraction
Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts.
Aspect and Sentiment Aware Abstractive Review Summarization
Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction.
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge
In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains.
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge.
Ranked #4 on
Sentiment Analysis
on MR
Investigating Capsule Networks with Dynamic Routing for Text Classification
In this study, we explore capsule networks with dynamic routing for text classification.
Ranked #6 on
Sentiment Analysis
on MR
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network.
Generative Adversarial Network for Abstractive Text Summarization
In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization.
Ranked #5 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters
We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters.
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
In this paper, we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking.
Context-aware System Service Call-oriented Symbolic Execution of Android Framework with Application to Exploit Generation
We present novel ideas and techniques to resolve the challenges, and have built the first system for symbolic execution of Android Framework.
Cryptography and Security Software Engineering
Temporal Dynamic Appearance Modeling for Online Multi-Person Tracking
The temporal dynamic makes a sufficient complement to the spatial structure of varying appearances in the feature space, which significantly improves the affinity measurement between trajectories and detections.
Ordering-sensitive and Semantic-aware Topic Modeling
In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling.
Relaxed Clipping: A Global Training Method for Robust Regression and Classification
We present a generic procedure that can be applied to standard loss functions and demonstrate improved robustness in regression and classification problems.
