9 citations · 15 across the 3 of their papers we have counts for
7 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…
Neural Ranking Models for Temporal Dependency Structure Parsing
Yuchen Zhang, Nianwen Xue
We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a te…
Structured Interpretation of Temporal Relations
Yuchen Zhang, Nianwen Xue
Temporal relations between events and time expressions in a document are often modeled in an unstructured manner where relations between individual pairs of time expressions and ev…
Addressing the Data Sparsity Issue in Neural AMR Parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea +1
Neural attention models have achieved great success in different NLP tasks. How- ever, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue.…