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Found 35 papers, 19 papers with code
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 Generative Framework with Prompt-guided Attention for Low-resource NER
Most existing 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.
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
Named Entity Recognition
on CMeEE
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 DocRED
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
Recently, prompt-tuning has achieved promising results for certain few-shot classification tasks.
Ranked #4 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.
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.
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.
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 has been demonstrated to be highly beneficial to enhance relation extraction models, but it 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.
Contrastive Triple Extraction with Generative Transformer
In this paper, we revisit the end-to-end triple extraction task for sequence generation.
Ranked #6 on
Relation Extraction
on NYT
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.
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.