116 citations · 246 across the 18 of their papers we have counts for
18 papers
Multi-level Contrastive Learning Framework for Sequential Recommendation
Ziyang Wang, Huoyu Liu, Wei Wei +5
Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted t…
Know Deeper: Knowledge-Conversation Cyclic Utilization Mechanism for Open-domain Dialogue Generation
Yajing Sun, Yue Hu, Luxi Xing +2
End-to-End intelligent neural dialogue systems suffer from the problems of generating inconsistent and repetitive responses. Existing dialogue models pay attention to unilaterally…
Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering
Luxi Xing, Yue Hu, Jing Yu +2
It is prevalent to utilize external knowledge to help machine answer questions that need background commonsense, which faces a problem that unlimited knowledge will transmit noisy…
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension
Wei Peng, Yue Hu, Jing Yu +4
Question answering systems usually use keyword searches to retrieve potential passages related to a question, and then extract the answer from passages with the machine reading com…
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation
Yuqiang Xie, Luxi Xing, Wei Peng +1
This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning. To help our model better represent and understand abstra…
Learning User Representations with Hypercuboids for Recommender Systems
Shuai Zhang, Huoyu Liu, Aston Zhang +6
Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifical…
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +3
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid cou…
CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
Jing Yu, Yuan Chai, Yujing Wang +2
Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when f…
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering
Jing Yu, Zihao Zhu, Yujing Wang +3
Knowledge-based Visual Question Answering (KVQA) requires external knowledge beyond the visible content to answer questions about an image. This ability is challenging but indispen…
DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
Xiaoze Jiang, Jing Yu, Yajing Sun +4
Visual Dialogue task requires an agent to be engaged in a conversation with human about an image. The ability of generating detailed and non-repetitive responses is crucial for the…
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE
Luxi Xing, Yuqiang Xie, Yue Hu +1
This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation. To clarify the intention for judgment and inject contrastive…
On Learning Universal Representations Across Languages
Xiangpeng Wei, Rongxiang Weng, Yue Hu +3
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks. However, exis…
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering
Zihao Zhu, Jing Yu, Yujing Wang +3
Fact-based Visual Question Answering (FVQA) requires external knowledge beyond visible content to answer questions about an image, which is challenging but indispensable to achieve…
Multiscale Collaborative Deep Models for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +3
Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a Multi…
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue
Xiaoze Jiang, Jing Yu, Zengchang Qin +4
Different from Visual Question Answering task that requires to answer only one question about an image, Visual Dialogue involves multiple questions which cover a broad range of vis…
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering
Zhuoqian Yang, Zengchang Qin, Jing Yu +1
One of the key issues of Visual Question Answering (VQA) is to reason with semantic clues in the visual content under the guidance of the question, how to model relational semantic…
Semantic Modeling of Textual Relationships in Cross-Modal Retrieval
Jing Yu, Chenghao Yang, Zengchang Qin +3
Feature modeling of different modalities is a basic problem in current research of cross-modal information retrieval. Existing models typically project texts and images into one em…
Mining and Analyzing the Future Works in Scientific Articles
Yue Hu, Xiaojun Wan
Future works in scientific articles are valuable for researchers and they can guide researchers to new research directions or ideas. In this paper, we mine the future works in scie…