no code implementations • 12 Sep 2023 • Di Lu, Zhongping Liang, Caixia Yuan, Xiaojie Wang
This paper presents a novel approach to address the Entity Recognition and Linking Challenge at NLPCC 2015.
1 code implementation • 23 Jul 2023 • Yuzhao Mao, Di Lu, Xiaojie Wang, Yang Zhang
This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances.
Ranked #17 on Emotion Recognition in Conversation on IEMOCAP
1 code implementation • 10 Jul 2023 • Di Lu, Shihao Ran, Joel Tetreault, Alejandro Jaimes
In this paper, we propose QGA-EE, which enables a Question Generation (QG) model to generate questions that incorporate rich contextual information instead of using fixed templates.
1 code implementation • 30 Jun 2023 • Shihao Ran, Di Lu, Joel Tetreault, Aoife Cahill, Alejandro Jaimes
The ability to conduct retrospective analyses of attacks on human rights defenders over time and by location is important for humanitarian organizations to better understand historical or ongoing human rights violations and thus better manage the global impact of such events.
1 code implementation • 20 Dec 2022 • Liang Ma, Shuyang Cao, Robert L. Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes
The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them.
no code implementations • NAACL 2021 • Eleftheria Briakou, Di Lu, Ke Zhang, Joel Tetreault
We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian.
no code implementations • NAACL (TextGraphs) 2021 • Sanghamitra Dutta, Liang Ma, Tanay Kumar Saha, Di Lu, Joel Tetreault, Alejandro Jaimes
Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection.
1 code implementation • 8 Apr 2021 • Eleftheria Briakou, Di Lu, Ke Zhang, Joel Tetreault
We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian.
no code implementations • COLING 2020 • Yova Kementchedjhieva, Di Lu, Joel Tetreault
News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences.
no code implementations • 7 Jun 2020 • Ayan Mukhopadhyay, Geoffrey Pettet, Sayyed Vazirizade, Di Lu, Said El Said, Alex Jaimes, Hiba Baroud, Yevgeniy Vorobeychik, Mykel Kochenderfer, Abhishek Dubey
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems.
no code implementations • ACL 2020 • Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, Shih-Fu Chang
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents.
no code implementations • LREC 2020 • Di Lu, Ananya Subburathinam, Heng Ji, Jonathan May, Shih-Fu Chang, Avi Sil, Clare Voss
Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging.
no code implementations • IJCNLP 2019 • Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss
The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages.
no code implementations • ACL 2018 • Di Lu, Leonardo Neves, Vitor Carvalho, Ning Zhang, Heng Ji
Everyday billions of multimodal posts containing both images and text are shared in social media sites such as Snapchat, Twitter or Instagram.
no code implementations • ACL 2018 • Ying Lin, Cash Costello, Boliang Zhang, Di Lu, Heng Ji, James Mayfield, Paul McNamee
We demonstrate two annotation platforms that allow an English speaker to annotate names for any language without knowing the language.
no code implementations • NAACL 2018 • Boliang Zhang, Ying Lin, Xiaoman Pan, Di Lu, Jonathan May, Kevin Knight, Heng Ji
We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap.
no code implementations • EMNLP 2018 • Di Lu, Spencer Whitehead, Lifu Huang, Heng Ji, Shih-Fu Chang
Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images.
no code implementations • IJCNLP 2017 • Boliang Zhang, Di Lu, Xiaoman Pan, Ying Lin, Halidanmu Abudukelimu, Heng Ji, Kevin Knight
Current supervised name tagging approaches are inadequate for most low-resource languages due to the lack of annotated data and actionable linguistic knowledge.
no code implementations • NAACL 2016 • Di Lu, Clare Voss, Fangbo Tao, Xiang Ren, Rachel Guan, Rostyslav Korolov, Tongtao Zhang, Dongang Wang, Hongzhi Li, Taylor Cassidy, Heng Ji, Shih-Fu Chang, Jiawei Han, William Wallace, James Hendler, Mei Si, Lance Kaplan