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20152022
most citedCATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network

194 citations · 424 across the 14 of their papers we have counts for

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6 papers · 1 filter

cs.CL20203 cited

MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization

Canwen Xu, Jiaxin Pei, Hongtao Wu +2

Recently, large-scale datasets have vastly facilitated the development in nearly all domains of Natural Language Processing. However, there is currently no cross-task dataset in NL…

cs.CL2019

CASE: Context-Aware Semantic Expansion

Jialong Han, Aixin Sun, Haisong Zhang +2

In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well fit…

cs.CL2019

Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders

Yu Duan, Canwen Xu, Jiaxin Pei +2

Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generate…

cs.CL2019

Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Canwen Xu, Feiyang Wang, Jialong Han +1

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in mi…

cs.CL20191 cited

Review-Driven Answer Generation for Product-Related Questions in E-Commerce

Shiqian Chen, Chenliang Li, Feng Ji +2

The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify…

cs.CL201921 cited

DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets

Canwen Xu, Jing Li, Xiangyang Luo +3

In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets. Recognizing a…