87 citations · 166 across the 14 of their papers we have counts for
14 papers
Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing
Ting Zhang, Zhuang Chen, Ming Zhong +1
Emotion recognition in conversation, which aims to predict the emotion for all utterances, has attracted considerable research attention in recent years. It is a challenging task s…
Prompting Large Language Models for Counterfactual Generation: An Empirical Study
Yongqi Li, Mayi Xu, Xin Miao +2
Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks. However, their ability to generate counterfactual…
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction
Wanli Li, Tieyun Qian, Yi Song +4
Zero-shot Relation Triplet Extraction (ZeroRTE) aims to extract relation triplets from texts containing unseen relation types. This capability benefits various downstream informati…
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
Yongqi Li, Yu Yu, Tieyun Qian
Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the overdetected false spans at the span detection s…
Scalable Time-Range k-Core Query on Temporal Graphs(Full Version)
Junyong Yang, Ming Zhong, Yuanyuan Zhu +3
Querying cohesive subgraphs on temporal graphs with various time constraints has attracted intensive research interests recently. In this paper, we study a novel Temporal k-Core Qu…
Automatically Generating Counterfactuals for Relation Classification
Mi Zhang, Tieyun Qian, Ting Zhang
The goal of relation classification (RC) is to extract the semantic relations between/among entities in the text. As a fundamental task in natural language processing, it is crucia…
From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction
Wanli Li, Tieyun Qian
Lack of labeled data is a main obstacle in relation extraction. Semi-supervised relation extraction (SSRE) has been proven to be a promising way for this problem through annotating…
Multi-Scale Feature and Metric Learning for Relation Extraction
Mi Zhang, Tieyun Qian
Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical feature…
Intent Disentanglement and Feature Self-supervision for Novel Recommendation
Tieyun Qian, Yile Liang, Qing Li +3
One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail i…
Recommending Accurate and Diverse Items Using Bilateral Branch Network
Yile Liang, Tieyun Qian
Recommender systems have played a vital role in online platforms due to the ability of incorporating users' personal tastes. Beyond accuracy, diversity has been recognized as a key…
Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction
Wanli Li, Tieyun Qian
Manual annotation of the labeled data for relation extraction is time-consuming and labor-intensive. Semi-supervised methods can offer helping hands for this problem and have arous…
Seq2seq Translation Model for Sequential Recommendation
Ke Sun, Tieyun Qian
The context information such as product category plays a critical role in sequential recommendation. Recent years have witnessed a growing interest in context-aware sequential reco…
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
Tieyun Qian, Yile Liang, Qing Li
Matrix completion is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially grap…
Learning Spatiotemporal-Aware Representation for POI Recommendation
Bei Liu, Tieyun Qian, Bing Liu +3
The wide spread of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Recent advanc…