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Found 68 papers, 49 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.
Molecular Language Model as Multi-task Generator
We further propose multi-task molecular prefix tuning across several molecular generation tasks and different molecular domains (synthetic & natural products) with a self-feedback mechanism.
Editing Language Model-based Knowledge Graph Embeddings
We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task.
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.
A Concept Knowledge Graph for User Next Intent Prediction at Alipay
This paper illustrates the technologies of user next intent prediction with a concept knowledge graph.
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.
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.
Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction
In this paper, we propose a novel approach of schema-aware Reference As Prompt (RAP), which dynamically leverage schema and knowledge inherited from global (few-shot) training data for each sample.
Multimodal Analogical Reasoning over Knowledge Graphs
Analogical reasoning is fundamental to human cognition and holds an important place in various fields.
PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application
Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information.
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.
Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition
Our module can work seamlessly with the existing action classification model.
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge.
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.
Relphormer: Relational Graph Transformer for Knowledge Graph Representations
To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer.
Ranked #1 on
Link Prediction
on FB15k-237
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.
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.
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.
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.
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.
Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective
Knowledge Extraction (KE), aiming to extract structural information from unstructured texts, often suffers from data scarcity and emerging unseen types, i. e., low-resource scenarios.
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 #43 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.
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they struggle to reason rare or emerging unseen entities.
Ranked #1 on
Link Prediction
on FB15k-237-ind
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
+3
LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training
Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation.
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.
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets.
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 SST-2 Binary classification
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.
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Medical Relation Extraction
on CMeIE
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 #3 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.
Field Embedding: A Unified Grain-Based Framework for Word Representation
Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings.
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.
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
Knowledge Graph (KG) alignment is to discover the mappings (i. e., equivalent entities, relations, and others) between two KGs.
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.
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.
Noisy-Labeled NER with Confidence Estimation
This work studies NER under a noisy labeled setting with calibrated confidence estimation.
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.
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)
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.
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.
Finding Influential Instances for Distantly Supervised Relation Extraction
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise.
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 #8 on
Relation Extraction
on WebNLG
Conceptualized Representation Learning for Chinese Biomedical Text Mining
In this paper, we investigate how the recently introduced pre-trained language model BERT can be adapted for Chinese biomedical corpora and propose a novel conceptualized representation learning approach.
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.
Context-aware Deep Model for Entity Recommendation in Search Engine at Alibaba
Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today's search engines.
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.
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.
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.
