370 citations · 1.5k across the 61 of their papers we have counts for
19 papers · 1 filter
LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training
Shumin Deng, Jiacheng Yang, Hongbin Ye +6
Natural language generation from structured data mainly focuses on surface-level descriptions, suffering from uncontrollable content selection and low fidelity. Previous works leve…
Learning to Ask for Data-Efficient Event Argument Extraction
Hongbin Ye, Ningyu Zhang, Zhen Bi +5
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and e…
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
Chengxi Li, Feiyu Gao, Jiajun Bu +8
Aspect-based sentiment analysis (ABSA) is an emerging fine-grained sentiment analysis task that aims to extract aspects, classify corresponding sentiment polarities and find opinio…
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
Xiang Chen, Lei Li, Shumin Deng +6
Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data. Existing dominant approaches usually s…
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Ningyu Zhang, Luoqiu Li, Xiang Chen +5
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners. However, their eff…
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Hengyi Zheng, Rui Wen, Xi Chen +7
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer fro…
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen +4
Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embeddin…
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang, Mosha Chen, Zhen Bi +20
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical langua…
Document-level Relation Extraction as Semantic Segmentation
Ningyu Zhang, Xiang Chen, Xin Xie +6
Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the en…
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Ningyu Zhang, Qianghuai Jia, Shumin Deng +8
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-…
MLBiNet: A Cross-Sentence Collective Event Detection Network
Dongfang Lou, Zhilin Liao, Shumin Deng +2
We consider the problem of collectively detecting multiple events, particularly in cross-sentence settings. The key to dealing with the problem is to encode semantic information an…
OntoED: Low-resource Event Detection with Ontology Embedding
Shumin Deng, Ningyu Zhang, Luoqiu Li +5
Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, an…
Interventional Aspect-Based Sentiment Analysis
Zhen Bi, Ningyu Zhang, Ganqiang Ye +3
Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when enco…
Noisy-Labeled NER with Confidence Estimation
Kun Liu, Yao Fu, Chuanqi Tan +4
Recent studies in deep learning have shown significant progress in named entity recognition (NER). Most existing works assume clean data annotation, yet a fundamental challenge in…
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
Xiang Chen, Ningyu Zhang, Xin Xie +6
Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into th…
Disentangled Contrastive Learning for Learning Robust Textual Representations
Xiang Chen, Xin Xie, Zhen Bi +4
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-o…
Text-guided Legal Knowledge Graph Reasoning
Luoqiu Li, Zhen Bi, Hongbin Ye +3
Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provi…
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Luoqiu Li, Xiang Chen, Zhen Bi +6
Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks. Thu…
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
Xin Xie, Xiangnan Chen, Xiang Chen +4
This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and t…