36 citations · 74 across the 14 of their papers we have counts for
14 papers
DocTER: Evaluating Document-based Knowledge Editing
Suhang Wu, Ante Wang, Minlong Peng +4
Knowledge editing aims to correct outdated or inaccurate knowledge in neural networks. In this paper, we explore knowledge editing using easily accessible documents instead of manu…
Actively Supervised Clustering for Open Relation Extraction
Jun Zhao, Yongxin Zhang, Qi Zhang +4
Current clustering-based Open Relation Extraction (OpenRE) methods usually adopt a two-stage pipeline. The first stage simultaneously learns relation representations and assignment…
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction
Jun Zhao, Wenyu Zhan, Xin Zhao +6
Semantic matching is a mainstream paradigm of zero-shot relation extraction, which matches a given input with a corresponding label description. The entities in the input should ex…
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
Xuanting Chen, Junjie Ye, Can Zu +7
The GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities. Ho…
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion
Jian Song, Di Liang, Rumei Li +5
Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also shown general benefits in…
NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language
Faysal Hossain Shezan, Yingjie Lao, Minlong Peng +3
The recent privacy leakage incidences and the more strict policy regulations demand a much higher standard of compliance for companies and mobile apps. However, such obligations al…
Under-confidence Backdoors Are Resilient and Stealthy Backdoors
Minlong Peng, Zidi Xiong, Quang H. Nguyen +3
By injecting a small number of poisoned samples into the training set, backdoor attacks aim to make the victim model produce designed outputs on any input injected with pre-designe…
Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling
Yicheng Zou, Lujun Zhao, Yangyang Kang +7
In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to…
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis
Minlong Peng, Qi Zhang, Xuanjing Huang
Cross-domain sentiment analysis is currently a hot topic in the research and engineering areas. One of the most popular frameworks in this field is the domain-invariant representat…
Simplify the Usage of Lexicon in Chinese NER
Ruotian Ma, Minlong Peng, Qi Zhang +1
Recently, many works have tried to augment the performance of Chinese named entity recognition (NER) using word lexicons. As a representative, Lattice-LSTM (Zhang and Yang, 2018) h…
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
Minlong Peng, Xiaoyu Xing, Qi Zhang +2
In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive…
Learning Task-specific Representation for Novel Words in Sequence Labeling
Minlong Peng, Qi Zhang, Xiaoyu Xing +3
Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor…
Address Instance-level Label Prediction in Multiple Instance Learning
Minlong Peng, Qi Zhang
\textit{Multiple Instance Learning} (MIL) is concerned with learning from bags of instances, where only bag labels are given and instance labels are unknown. Existent approaches in…
Long Short-Term Memory with Dynamic Skip Connections
Tao Gui, Qi Zhang, Lujun Zhao +4
In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing lo…