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Found 77 papers, 17 papers with code
Effect Generation Based on Causal Reasoning
Given an input cause sentence, a causal subgraph is retrieved from the event causality network and is encoded with the graph attention mechanism, in order to support better reasoning of the potential effects.
MMCoQA: Conversational Question Answering over Text, Tables, and Images
In this paper, we hence define a novel research task, i. e., multimodal conversational question answering (MMCoQA), aiming to answer users’ questions with multimodal knowledge sources via multi-turn conversations.
Event Graph based Sentence Fusion
Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence.
Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfer
As an emerging secure learning paradigm in leveraging cross-agency private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher.
Federated X-Armed Bandit
This work establishes the first framework of federated $\mathcal{X}$-armed bandit, where different clients face heterogeneous local objective functions defined on the same domain and are required to collaboratively figure out the global optimum.
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method
The experimental results show that the image denoising method of ZrO2 ceramic bearing ball surface defect based on cartoon-texture decomposition model can denoise while retaining the image details.
"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
In this paper, we explore how to bring interpretability to data-driven DSMD.
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning.
Federated Online Sparse Decision Making
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters.
Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution
Convolutional neural networks based single-image super-resolution (SISR) has made great progress in recent years.
CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation
In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data.
Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality
Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately.
Deep Dirichlet Process Mixture Models
In this paper we propose the deep Dirichlet process mixture (DDPM) model, which is an unsupervised method that simultaneously performs clustering and feature learning.
Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization
During the training stage, with teacher forcing these models are optimized to maximize the likelihood of the gold summary given the gold summary tokens as input to the decoder, while at inference the given tokens are replaced by the generated tokens.
PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)
In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context.
Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization
In this paper, we make the key delineation on the roles of resolution and statistical uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more general analysis and a more efficient algorithm design.
Data Distillation for Text Classification
In this paper, we study a related but orthogonal issue, data distillation, which aims to distill the knowledge from a large training dataset down to a smaller and synthetic one.
A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering
Moreover, in order to collect more complementary information in the historical context, we also propose to incorporate the multi-round relevance feedback technique to explore the impact of the retrieval context on current question understanding.
Conversational Question Answering
Open-Domain Question Answering
A Simple Unified Framework for High Dimensional Bandit Problems
Stochastic high dimensional bandit problems with low dimensional structures are useful in different applications such as online advertising and drug discovery.
Incremental Knowledge Based Question Answering
In the past years, Knowledge-Based Question Answering (KBQA), which aims to answer natural language questions using facts in a knowledge base, has been well developed.
On the Marginal Regret Bound Minimization of Adaptive Methods
Numerous adaptive algorithms such as AMSGrad and Radam have been proposed and applied to deep learning recently.
Variance Reduction on General Adaptive Stochastic Mirror Descent
In this work, we investigate the idea of variance reduction by studying its properties with general adaptive mirror descent algorithms in nonsmooth nonconvex finite-sum optimization problems.
Stochastic Deep Gaussian Processes over Graphs
In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains.
Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
We also introduce a hierarchical structure, which incorporates the multi-level of granularities of the textual information into the model.
Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech
Accent conversion (AC) transforms a non-native speaker's accent into a native accent while maintaining the speaker's voice timbre.
Increased-confidence adversarial examples for deep learning counter-forensics
Transferability of adversarial examples is a key issue to apply this kind of attacks against multimedia forensics (MMF) techniques based on Deep Learning (DL) in a real-life setting.
AdaX: Adaptive Gradient Descent with Exponential Long Term Memory
Although adaptive optimization algorithms such as Adam show fast convergence in many machine learning tasks, this paper identifies a problem of Adam by analyzing its performance in a simple non-convex synthetic problem, showing that Adam's fast convergence would possibly lead the algorithm to local minimums.
How Does BN Increase Collapsed Neural Network Filters?
This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result.
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization
Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL description for some given MRs.
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.
Ranked #1 on
Recommendation Systems
on Dianping-Food
Knowledge Graph Convolutional Networks for Recommender Systems
To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information.
Ranked #1 on
Click-Through Rate Prediction
on Book-Crossing
When Collaborative Filtering Meets Reinforcement Learning
In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations.
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks
To enhance the emotion discriminativeness of words in textual feature extraction, we propose Emotional Word Embedding (EWE) to learn text representations by jointly considering their semantics and emotions.
Incorporating Relevant Knowledge in Context Modeling and Response Generation
To sustain engaging conversation, it is critical for chatbots to make good use of relevant knowledge.
Meta-path Augmented Response Generation
We propose a chatbot, namely Mocha to make good use of relevant entities when generating responses.
Variational Autoregressive Decoder for Neural Response Generation
Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation.
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation
Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems.
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably.
Ranked #19 on
Text Summarization
on GigaWord
Large scale classification in deep neural network with Label Mapping
Therefore, it is infeasible to solve the multi-class classification problem using deep neural network when the number of classes are huge.
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
We evaluate our approach on two review datasets, Yelp and Amazon.
Ranked #5 on
Unsupervised Text Style Transfer
on Yelp
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.
Ranked #2 on
Click-Through Rate Prediction
on Book-Crossing
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words.
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
The decoding of the complex structure model is regularized by the additionally trained simple structure model.
Faithful to the Original: Fact Aware Neural Abstractive Summarization
While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system.
Ranked #18 on
Text Summarization
on GigaWord
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering
The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets.
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization.
A Conditional Variational Framework for Dialog Generation
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems.
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted.
Mode Regularized Generative Adversarial Networks
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes.
Content-based Influence Modeling for Opinion Behavior Prediction
The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors.
Joint Copying and Restricted Generation for Paraphrase
In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder.
Improving Multi-Document Summarization via Text Classification
Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents.
Emotion Corpus Construction Based on Selection from Hashtags
Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels.
AttSum: Joint Learning of Focusing and Summarization with Neural Attention
Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization.
TGSum: Build Tweet Guided Multi-Document Summarization Dataset
Both informativeness and readability of the collected summaries are verified by manual judgment.
Component-Enhanced Chinese Character Embeddings
Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English.
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines
We then revisit Boltzmann machines (BM) from a model selection perspective and theoretically show that both the fully visible BM (VBM) and the BM with hidden units can be derived from the general binary multivariate distribution using the CIF principle.
Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principle
Typical dimensionality reduction methods focus on directly reducing the number of random variables while retaining maximal variations in the data.
