Search Results for author:
Found 39 papers, 18 papers with code
DESED: Dialogue-based Explanation for Sentence-level Event Detection
Many recent sentence-level event detection efforts focus on enriching sentence semantics, e. g., via multi-task or prompt-based learning.
Improving Embedding-based Large-scale Retrieval via Label Enhancement
Current embedding-based large-scale retrieval models are trained with 0-1 hard label that indicates whether a query is relevant to a document, ignoring rich information of the relevance degree.
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework
Recently, stacked networks show powerful performance in Image Restoration, such as challenging motion deblurring problems.
Label Smoothing for Text Mining
This paper presents a novel keyword-based LS method to automatically generate soft labels from hard labels via exploiting the relevance between labels and text instances.
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.
Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction
Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them.
Ranked #1 on
Relation Extraction
on TACRED-Revisited
Sequence Generation with Label Augmentation for Relation Extraction
Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models.
Ranked #1 on
Relation Extraction
on sciERC-sent
MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts
As a pioneering exploration that expands event detection to the scenarios involving informal and heterogeneous texts, we propose a new large-scale Chinese event detection dataset based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.
Consistent Direct Time-of-Flight Video Depth Super-Resolution
Direct time-of-flight (dToF) sensors are promising for next-generation on-device 3D sensing.
ShadowDiffusion: Diffusion-based Shadow Removal using Classifier-driven Attention and Structure Preservation
ShadowDiffusion focuses on single-image shadow removal, even in the presence of soft and self shadows.
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
A recent trend is to use Transformer or its variants in music generation, which is, however, suboptimal, because the full attention cannot efficiently model the typically long music sequences (e. g., over 10, 000 tokens), and the existing models have shortcomings in generating musical repetition structures.
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning.
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
Graph Kernels Based on Multi-scale Graph Embeddings
To mitigate the two challenges, we propose a novel graph kernel called the Multi-scale Path-pattern Graph kernel (MPG), at the heart of which is the multi-scale path-pattern node feature map.
A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example
Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e. g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry.
Incorporating Heterophily into Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) often assume strong homophily in graphs, seldom considering heterophily which means connected nodes tend to have different class labels and dissimilar features.
Graph Neural Diffusion Networks for Semi-supervised Learning
To solve these two issues, we propose a new graph neural network called GND-Nets (for Graph Neural Diffusion Networks) that exploits the local and global neighborhood information of a vertex in a single layer.
Modeling Human-AI Team Decision Making
AI and humans bring complementary skills to group deliberations.
Frequency-Aware Contrastive Learning for Neural Machine Translation
Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective.
Temporally Consistent Online Depth Estimation in Dynamic Scenes
We present a framework named Consistent Online Dynamic Depth (CODD) to produce temporally consistent depth estimates in dynamic scenes in an online setting.
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation
Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs.
Ranked #18 on
Semantic Segmentation
on Cityscapes val
Deep Embedded K-Means Clustering
To this end, we discard the decoder and propose a greedy method to optimize the representation.
Ranked #1 on
Deep Clustering
on Coil-20
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation
This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models.
Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting
Legal judgment prediction(LJP) is an essential task for legal AI.
Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder
Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE).
QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval
The embedding-based large-scale query-document retrieval problem is a hot topic in the information retrieval (IR) field.
Multi-Hop Transformer for Document-Level Machine Translation
Document-level neural machine translation (NMT) has proven to be of profound value for its effectiveness on capturing contextual information.
Multi-view Inference for Relation Extraction with Uncertain Knowledge
Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE) tasks.
Ranked #31 on
Relation Extraction
on DocRED
(using extra training data)
Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach
Code summaries are brief natural language descriptions of source code pieces.
SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint
Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry.
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction
Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relational facts.
Learning Deep Graph Representations via Convolutional Neural Networks
The learned deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood.
Incorporating User's Preference into Attributed Graph Clustering
In contrast to global clustering, local clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs).
Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance.
Tree++: Truncated Tree Based Graph Kernels
At the heart of Tree++ is a graph kernel called the path-pattern graph kernel.
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data
In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class.
PKUSE at SemEval-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network
This paper presents the system in SemEval-2019 Task 3, {``}EmoContext: Contextual Emotion Detection in Text{''}.
Deep Dynamic Boosted Forest
Specically, we propose to measure the quality of each leaf node of every decision tree in the random forest to determine hard examples.
