9 citations · 19 across the 8 of their papers we have counts for
8 papers
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
Jingxuan Yang, Kerui Xu, Jun Xu +5
In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and…
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields
Jingxuan Yang, Kerui Xu, Jun Xu +5
Pronouns are often dropped in Chinese conversations and recovering the dropped pronouns is important for NLP applications such as Machine Translation. Existing approaches usually f…
Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents
Jingxuan Yang, Jianzhuo Tong, Si Li +3
Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context. Recovering drop…
Pre-training of Context-aware Item Representation for Next Basket Recommendation
Jingxuan Yang, Jun Xu, Jianzhuo Tong +3
Next basket recommendation, which aims to predict the next a few items that a user most probably purchases given his historical transactions, plays a vital role in market basket an…
From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information
Hengru Xu, Shen Li, Renfen Hu +2
Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout metho…
Improving Cross-domain Recommendation through Probabilistic Cluster-level Latent Factor Model--Extended Version
Siting Ren, Sheng Gao
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearin…
Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks
Sheng Gao, Ludovic Denoyer, Patrick Gallinari
This paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While common lin…
Modeling Relational Data via Latent Factor Blockmodel
Sheng Gao, Ludovic Denoyer, Patrick Gallinari
In this paper we address the problem of modeling relational data, which appear in many applications such as social network analysis, recommender systems and bioinformatics. Previou…