NLPExplorer

Junyi Jessy Li

Number of Papers:- 67

Number of Citations:- 36

First ACL Paper:- 2014

Latest ACL Paper:- 2024

Venues:-

DADC

NAACL

SIGDIAL

CoNLL

CRAC

COLING

EMNLP

Findings

LREC

NLP4Prog

ACL

CODI

EACL

IJCNLP

INLG

Co-Authors:-

Similar Authors:-

Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024) CODI WS

Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion NAACL

Smriti Singh | Cornelia Caragea | Junyi Jessy Li |

FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence ACL

InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification ACL

Which questions should I answer? Salience Prediction of Inquisitive Questions EMNLP

Learning to Refine with Fine-Grained Natural Language Feedback F i n d i n g s - E M N L P

Manya Wadhwa | Xinyu Zhao | Junyi Jessy Li | Greg Durrett |

Do they mean ‘us’? Interpreting Referring Expression variation under Intergroup Bias F i n d i n g s - E M N L P

Detection and Measurement of Syntactic Templates in Generated Text EMNLP

Chantal Shaib | Yanai Elazar | Junyi Jessy Li | Byron C Wallace |

How people talk about each other: Modeling Generalized Intergroup Bias and Emotion EACL

QUDeval: The Evaluation of Questions Under Discussion Discourse Parsing EMNLP

Yating Wu | Ritika Mangla | Greg Durrett | Junyi Jessy Li |

Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models F i n d i n g s - E M N L P

Hongli Zhan | Desmond Ong | Junyi Jessy Li |

Multilingual Simplification of Medical Texts EMNLP

Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion F i n d i n g s - A C L

Elaborative Simplification as Implicit Questions Under Discussion EMNLP

Yating Wu | William Sheffield | Kyle Mahowald | Junyi Jessy Li |

Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias F i n d i n g s - A C L

Venkata Subrahmanyan Govindarajan | David Beaver | Kyle Mahowald | Junyi Jessy Li |

Summarizing, Simplifying, and Synthesizing Medical Evidence using GPT-3 (with Varying Success) ACL

Unsupervised Extractive Summarization of Emotion Triggers ACL

Tiberiu Sosea | Hongli Zhan | Junyi Jessy Li | Cornelia Caragea |

How Do We Answer Complex Questions: Discourse Structure of Long-form Answers ACL

Fangyuan Xu | Junyi Jessy Li | Eunsol Choi |

ProtoTEx: Explaining Model Decisions with Prototype Tensors ACL

Evaluating Factuality in Text Simplification ACL

Ashwin Devaraj | William Sheffield | Byron Wallace | Junyi Jessy Li |

Training Dynamics for Text Summarization Models ACL Findings

Tanya Goyal | Jiacheng Xu | Junyi Jessy Li | Greg Durrett |

Learning to Describe Solutions for Bug Reports Based on Developer Discussions ACL Findings

Sheena Panthaplackel | Junyi Jessy Li | Milos Gligoric | Ray Mooney |

Impact of Evaluation Methodologies on Code Summarization ACL

Political Ideology and Polarization: A Multi-dimensional Approach NAACL

longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks. DADC NAACL

Emotion analysis and detection during COVID-19 LREC

The Role of Context and Uncertainty in Shallow Discourse Parsing COLING

Katherine Atwell | Remi Choi | Junyi Jessy Li | Malihe Alikhani |

Text Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus COLING

Zachary W. Taylor | Maximus H. Chu | Junyi Jessy Li |

Proceedings of the 3rd Workshop on Computational Approaches to Discourse CODI COLING CRAC WS

FALTE: A Toolkit for Fine-grained Annotation for Long Text Evaluation EMNLP

Tanya Goyal | Junyi Jessy Li | Greg Durrett |

Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts EMNLP

Hongli Zhan | Tiberiu Sosea | Cornelia Caragea | Junyi Jessy Li |

Using Developer Discussions to Guide Fixing Bugs in Software F i n d i n g s - E M N L P

Sheena Panthaplackel | Milos Gligoric | Junyi Jessy Li | Raymond Mooney |

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature ACL