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Findings - 2020
Total Papers:- 448
Total Papers accross all years:- 1873
Total Citations :- 0
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The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction
Hieu Minh Tran |
Minh Trung Nguyen |
Thien Huu Nguyen |
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Ziyi Yang |
Chenguang Zhu |
Robert Gmyr |
Michael Zeng |
Xuedong Huang |
Eric Darve |
Inserting Information Bottlenecks for Attribution in Transformers
Zhiying Jiang |
Raphael Tang |
Ji Xin |
Jimmy Lin |
Learning to Generalize for Sequential Decision Making
Xusen Yin |
Ralph Weischedel |
Jonathan May |
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning
Deepak Gupta |
Asif Ekbal |
Pushpak Bhattacharyya |
Towards Context-Aware Code Comment Generation
Xiaohan Yu |
Quzhe Huang |
Zheng Wang |
Yansong Feng |
Dongyan Zhao |
Query-Key Normalization for Transformers
Alex Henry |
Prudhvi Raj Dachapally |
Shubham Shantaram Pawar |
Yuxuan Chen |
Scene Graph Modification Based on Natural Language Commands
Xuanli He |
Quan Hung Tran |
Gholamreza Haffari |
Walter Chang |
Zhe Lin |
Trung Bui |
Franck Dernoncourt |
Nhan Dam |
MultiDM-GCN: Aspect-guided Response Generation in Multi-domain Multi-modal Dialogue System using Graph Convolutional Network
Mauajama Firdaus |
Nidhi Thakur |
Asif Ekbal |
Multi-hop Question Generation with Graph Convolutional Network
Dan Su |
Yan Xu |
Wenliang Dai |
Ziwei Ji |
Tiezheng Yu |
Pascale Fung |
Graph-to-Graph Transformer for Transition-based Dependency Parsing
Alireza Mohammadshahi |
James Henderson |
Narrative Text Generation with a Latent Discrete Plan
Harsh Jhamtani |
Taylor Berg-Kirkpatrick |
Resource-Enhanced Neural Model for Event Argument Extraction
Jie Ma |
Shuai Wang |
Rishita Anubhai |
Miguel Ballesteros |
Yaser Al-Onaizan |
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention
Daeun Lee |
Soyoung Park |
Jiwon Kang |
Daejin Choi |
Jinyoung Han |
Rethinking Topic Modelling: From Document-Space to Term-Space
Magnus Sahlgren |
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