Search Results for author:
Found 126 papers, 84 papers with code
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction.
Ruleformer: Context-aware Rule Mining over Knowledge Graph
Based on this idea, we propose a transformer-based rule mining approach, Ruleformer.
Large Knowledge Model: Perspectives and Challenges
Humankind's understanding of the world is fundamentally linked to our perception and cognition, with \emph{human languages} serving as one of the major carriers of \emph{world knowledge}.
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Besides, we design a new alignment objective to align the LLM preference with human preference, aiming to train a better LLM for real-scenario domain-specific QA to generate reliable and user-friendly answers.
Reliable Academic Conference Question Answering: A Study Based on Large Language Model
To address this need, we have developed the ConferenceQA dataset for 7 diverse academic conferences with human annotations.
Unveiling the Siren's Song: Towards Reliable Fact-Conflicting Hallucination Detection
Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread attention owing to their myriad of practical applications, yet their adoption has been constrained by issues of fact-conflicting hallucinations across web platforms.
Can We Edit Multimodal Large Language Models?
In this paper, we focus on editing Multimodal Large Language Models (MLLMs).
Making Large Language Models Perform Better in Knowledge Graph Completion
In this paper, we discuss how to incorporate the helpful KG structural information into the LLMs, aiming to achieve structrual-aware reasoning in the LLMs.
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation.
OceanGPT: A Large Language Model for Ocean Science Tasks
Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface.
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for LLMs.
Editing Personality for LLMs
This task seeks to adjust the models' responses to opinion-related questions on specified topics since an individual's personality often manifests in the form of their expressed opinions, thereby showcasing different personality traits.
Agents: An Open-source Framework for Autonomous Language Agents
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
When Do Program-of-Thoughts Work for Reasoning?
Although there are effective methods like program-of-thought prompting for LLMs which uses programming language to tackle complex reasoning tasks, the specific impact of code data on the improvement of reasoning capabilities remains under-explored.
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern.
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to the outdated/noisy data.
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.
Ranked #1 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v2
(using extra training data)
Graph Sampling-based Meta-Learning for Molecular Property Prediction
To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction.
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields.
Catalytic activity prediction
Chemical-Disease Interaction Extraction
+14
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Reasoning system dynamics is one of the most important analytical approaches for many scientific studies.
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning.
Editing Large Language Models: Problems, Methods, and Opportunities
Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
Making Language Models Better Tool Learners with Execution Feedback
Tools serve as pivotal interfaces that enable humans to understand and reshape the world.
Schema-adaptable Knowledge Graph Construction
This necessitates a system that can handle evolving schema automatically to extract information for KGC.
Knowledge Rumination for Pre-trained Language Models
Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted to integrate external knowledge into PLMs.
Continual Multimodal Knowledge Graph Construction
Multimodal Knowledge Graph Construction (MKGC) involves creating structured representations of entities and relations using multiple modalities, such as text and images.
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities.
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks.
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.
Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
In this paper, we use a set of general terminologies to unify these methods and refer to them as Knowledge Extrapolation.
Domain-Agnostic Molecular Generation with Self-feedback
However, despite the potential of language models in molecule generation, they face numerous challenges such as the generation of syntactically or chemically flawed molecules, narrow domain focus, and limitations in creating diverse and directionally feasible molecules due to a dearth of annotated data or external molecular databases.
One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER
Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain.
Editing Language Model-based Knowledge Graph Embeddings
To address this issue, we propose a new task of editing language model-based KG embeddings in this paper.
Analogical Inference Enhanced Knowledge Graph Embedding
And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding.
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.
Ranked #1 on
Entity Alignment
on FBYG15k
(using extra training data)
Reasoning with Language Model Prompting: A Survey
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.
On Analyzing the Role of Image for Visual-enhanced Relation Extraction
Multimodal relation extraction is an essential task for knowledge graph construction.
Generative Knowledge Graph Construction: A Review
Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction
With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance.
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
This paper presents an empirical study to build relation extraction systems in low-resource settings.
Relational Message Passing for Fully Inductive Knowledge Graph Completion
Subgraph reasoning with message passing is a promising and popular solution.
Multimodal Analogical Reasoning over Knowledge Graphs
Analogical reasoning is fundamental to human cognition and holds an important place in various fields.
Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks.
Neural-Symbolic Entangled Framework for Complex Query Answering
In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness.
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives.
Ranked #1 on
Zero-Shot Learning
on CUB-200-2011
Disentangled Ontology Embedding for Zero-shot Learning
In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.
Multi-modal Protein Knowledge Graph Construction and Applications
Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks.
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce
In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation.
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
We study the knowledge extrapolation problem to embed new components (i. e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting.
Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance.
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction.
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test.
Contrastive Demonstration Tuning for Pre-trained Language Models
Pretrained language models can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios.
Deep Reinforcement Learning for Entity Alignment
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies.
PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application
On the one hand, it could provide item knowledge services in a uniform way with service vectors for embedding-based and item-knowledge-related task models without accessing triple data.
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs.
Unleashing the Power of Transformer for Graphs
Our experiments demonstrate DET has achieved superior performance compared to the respective state-of-the-art methods in dealing with molecules, networks and knowledge graphs with various sizes.
Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective
Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry.
Prompt-Guided Injection of Conformation to Pre-trained Protein Model
To incorporate conformational knowledge to PTPMs, we propose an interaction-conformation prompt (IC prompt) that is learned through back-propagation with the protein-protein interaction task.
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Knowledge graph completion aims to address the problem of extending a KG with missing triples.
Ranked #49 on
Link Prediction
on FB15k-237
Ontology-enhanced Prompt-tuning for Few-shot Learning
Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.
OntoProtein: Protein Pretraining With Gene Ontology Embedding
We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph.
Kformer: Knowledge Injection in Transformer Feed-Forward Layers
In this work, we propose a simple model, Kformer, which takes advantage of the knowledge stored in PTMs and external knowledge via knowledge injection in Transformer FFN layers.
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
Attribute Extraction
Cross-Domain Named Entity Recognition
+4
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules
We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems.
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs.
LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training
Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation.
Molecular Contrastive Learning with Chemical Element Knowledge Graph
To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning.
Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings.
Principled Representation Learning for Entity Alignment
Embedding-based entity alignment (EEA) has recently received great attention.
Learning to Ask for Data-Efficient Event Argument Extraction
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.
Explaining Knowledge Graph Embedding via Latent Rule Learning
KGExplainer is a multi-hop reasoner learning latent rules for link prediction and is encouraged to behave similarly to KGEs during prediction through knowledge distillation.
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data.
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners.
Ranked #1 on
Few-Shot Learning
on CR
Knowledge Perceived Multi-modal Pretraining in E-commerce
In this paper, we address multi-modal pretraining of product data in the field of E-commerce.
Zero-shot Visual Question Answering using Knowledge Graph
Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need.
Ranked #1 on
Visual Question Answering (VQA)
on F-VQA
Document-level Relation Extraction as Semantic Segmentation
Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.
Ranked #4 on
Relation Extraction
on ReDocRED
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search.
OntoED: Low-resource Event Detection with Ontology Embedding
Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.
MLBiNet: A Cross-Sentence Collective Event Detection Network
We consider the problem of collectively detecting multiple events, particularly in cross-sentence settings.
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation.
Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph
We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.
Interventional Aspect-Based Sentiment Analysis
Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1 (v1) metric)
Disentangled Contrastive Learning for Learning Robust Textual Representations
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks.
Knowledge-aware Contrastive Molecular Graph Learning
Leveraging domain knowledge including fingerprints and functional groups in molecular representation learning is crucial for chemical property prediction and drug discovery.
Knowledge-aware Zero-Shot Learning: Survey and Perspective
Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a. k. a.
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM).
Ranked #1 on
Reading Comprehension
on ReCAM
(using extra training data)
OntoZSL: Ontology-enhanced Zero-shot Learning
The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e. g., features) from training classes (i. e., seen classes) to unseen classes.
Towards Principled Representation Learning for Entity Alignment
In this paper, we define a typical paradigm abstracted from the existing methods, and analyze how the representation discrepancy between two potentially-aligned entities is implicitly bounded by a predefined margin in the scoring function for embedding learning.
Towards Robust Textual Representations with Disentangled Contrastive Learning
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention
It is desirable to generate answer summaries for online search engines, particularly summaries that can reveal direct answers to questions.
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
To recognize unseen relations at test time, we explore the problem of zero-shot relation classification.
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings.
FedE: Embedding Knowledge Graphs in Federated Setting
Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.
OpenUE: An Open Toolkit of Universal Extraction from Text
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
Ranked #3 on
Joint Entity and Relation Extraction
on WebNLG
The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis
Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase, thereby leading to the deterioration of the tail performance.
On Robustness and Bias Analysis of BERT-based Relation Extraction
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks.
Contrastive Triple Extraction with Generative Transformer
In this paper, we revisit the end-to-end triple extraction task for sequence generation.
Ranked #9 on
Relation Extraction
on WebNLG
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.
Zero-shot Text Classification via Reinforced Self-training
In this situation, transferring from seen classes to unseen classes is extremely hard.
Ontology-guided Semantic Composition for Zero-Shot Learning
Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information.
Neural Entity Summarization with Joint Encoding and Weak Supervision
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts.
Generative Adversarial Zero-shot Learning via Knowledge Graphs
However, the side information of classes used now is limited to text descriptions and attribute annotations, which are in short of semantics of the classes.
Relation Adversarial Network for Low Resource Knowledge Graph Completion
Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations.
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs.
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification
Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes.
Ranked #1 on
Multi-Domain Sentiment Classification
on ARSC
Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning
However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision samples are usually noisy.
Augmenting Transfer Learning with Semantic Reasoning
We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings.
Learning to Decompose Compound Questions with Reinforcement Learning
Our model consists of two parts: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) three independent simple-question answerers that classify the corresponding relations for each simple question.
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available.
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]
Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide research attentions.
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding
This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.
Knowledge-based Transfer Learning Explanation
Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i. e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain.
Learning from Ontology Streams with Semantic Concept Drift
Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records.
