NLPExplorer

EACL - 2021

Total Papers:- 761

Total Papers accross all years:- 2768

Total Citations :- 0

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Estimating Subjective Crowd-Evaluations as an Additional Objective to Improve Natural Language Generation

Jakob Nyberg | Maike Paetzel | Ramesh Manuvinakurike |

evision PDF of 'A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News

Jasabanta Patro | Sabyasachee Baruah |

GX@DravidianLangTech-EACL2021: Multilingual Neural Machine Translation and Back-translation

Wanying Xie |

Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data

Tarek Naous | Wissam Antoun | Reem Mahmoud | Hazem Hajj |

Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks

Nisansa de Silva | Dejing Dou |

Amrita_CEN_NLP@DravidianLangTech-EACL2021: Deep Learning-based Offensive Language Identification in Malayalam, Tamil and Kannada

Sreelakshmi K | Premjith B | Soman Kp |

Two Training Strategies for Improving Relation Extraction over Universal Graph

Qin Dai | Naoya Inoue | Ryo Takahashi | Kentaro Inui |

‘Just because you are right, doesn’t mean I am wrong’: Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks

Query Generation for Multimodal Documents

ChainCQG: Flow-Aware Conversational Question Generation

Jing Gu | Mostafa Mirshekari | Zhou Yu | Aaron Sisto |

IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always hope in Transformers

JudithJeyafreedaAndrew@DravidianLangTech-EACL2021:Offensive language detection for Dravidian Code-mixed YouTube comments

Judith Jeyafreeda Andrew |

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

Isabelle Augenstein | Ivan Habernal |

AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization

Keping Bi | Rahul Jha | Bruce Croft | Asli Celikyilmaz |

Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning