EMNLP 2023 Conference Submissions

MailEx: Email Event and Argument Extraction
Saurabh Srivastava, Gaurav Singh, Shou Matsumoto, Ali K Raz, Paulo Costa, Joshua Campbell Poore, Ziyu Yao
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
MaNtLE: Model-agnostic Natural Language Explainer
Rakesh R Menon, Kerem Zaman, Shashank Srivastava
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
An Intent-based and Annotation-free Method for Duplicate Question Detection in CQA Forums
Yubo Shu, Hansu Gu, Peng Zhang, Tun Lu, Ning Gu
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
Sungryull Sohn, Yiwei Lyu, Anthony Zhe Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks
Hoang H Nguyen, Ye Liu, Chenwei Zhang, TAO ZHANG, Philip S. Yu
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models
Di Wu, Wasi Uddin Ahmad, Kai-Wei Chang
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
D$^2$TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization
Yunlong Liang, Fandong Meng, Jiaan Wang, Jinan Xu, Yufeng Chen, Jie Zhou
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
Shuhan Wu, Huaiyu Wan, Wei Chen, Yuting Wu, Junfeng Shen, Youfang Lin
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
PROSE: A Pronoun Omission Solution for Chinese-English Spoken Language Translation
Ke Wang, Xiutian Zhao, Yanghui Li, Wei Peng
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models
Hongli Zhan, Desmond Ong, Junyi Jessy Li
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Quantifying Character Similarity with Vision Transformers
Xinmei Yang, Abhishek Arora, Shao-Yu Jheng, Melissa Dell
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
LayoutDIT: Layout-Aware End-to-End Document Image Translation with Multi-Step Conductive Decoder
Zhiyang Zhang, Yaping Zhang, Yupu Liang, Lu Xiang, Yang Zhao, Yu Zhou, Chengqing Zong
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models
Haoxuan You, Rui Sun, Zhecan Wang, Long Chen, Gengyu Wang, Hammad Ayyubi, Kai-Wei Chang, Shih-Fu Chang
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Rethinking Word-Level Auto-Completion in Computer-Aided Translation
Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models
Haoran Wang, Kai Shu
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Hierarchical Catalogue Generation for Literature Review: A Benchmark
kun Zhu, Xiaocheng Feng, Xiachong Feng, Yingsheng Wu, Bing Qin
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
MMNMT: Modularizing Multilingual Neural Machine Translation with Flexibly Assembled MoE and Dense Blocks
Shangjie Li, Xiangpeng Wei, shaolin Zhu, Jun Xie, Baosong Yang, Deyi Xiong
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering
Inderjeet Jayakumar Nair, Shwetha S, Apoorv Saxena, Koustava Goswami
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Syntax-Aware Retrieval Augmented Code Generation
Xiangyu Zhang, Yu Zhou, Guang Yang, Taolue Chen
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
Weixiang Yan, Yuchen Tian, Yunzhe Li, Qian Chen, Wen Wang
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation
Cong-Duy T Nguyen, Thong Thanh Nguyen, Duc Anh Vu, Anh Tuan Luu
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback
Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang, Shuming Shi, Zhaopeng Tu
- Published: 23 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables
Xinyuan Lu, Liangming Pan, Qian Liu, Preslav Nakov, Min-Yen Kan
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Chinese Metaphorical Relation Extraction
Guihua Chen, Tiantian Wu, MiaoMiao Cheng, Xu Han, Jiefu Gong, Shijin Wang, Wei Song
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models
Yufan Wu, Yinghui He, Yilin Jia, Rada Mihalcea, Yulong Chen, Naihao Deng
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone