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20192024
most citedLegal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting

51 citations · 55 across the 9 of their papers we have counts for

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14 papers · 1 filter

cs.CL2021

Dialogue Inspectional Summarization with Factual Inconsistency Awareness

Leilei Gan, Yating Zhang, Kun Kuang +5

Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the out…

cs.CL2021

A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis

Jiawei Liu, Kaisong Song, Yangyang Kang +5

Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital appreh…

cs.CL2021

A Neural Conversation Generation Model via Equivalent Shared Memory Investigation

Changzhen Ji, Yating Zhang, Xiaozhong Liu +4

Conversation generation as a challenging task in Natural Language Generation (NLG) has been increasingly attracting attention over the last years. A number of recent works adopted…

cs.CL202151 cited

Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting

Luyao Ma, Yating Zhang, Tianyi Wang +4

Legal judgment prediction(LJP) is an essential task for legal AI. While prior methods studied on this topic in a pseudo setting by employing the judge-summarized case narrative as…

cs.CL2021

Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning

Fubang Zhao, Zhuoren Jiang, Yangyang Kang +2

Relational fact extraction aims to extract semantic triplets from unstructured text. In this work, we show that all of the relational fact extraction models can be organized accord…

cs.CL20202 cited

Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff

Jiawei Liu, Zhe Gao, Yangyang Kang +5

Is chatbot able to completely replace the human agent? The short answer could be - "it depends...". For some challenging cases, e.g., dialogue's topical spectrum spreads beyond the…