Search Results for author: Zhexiong Liu

Found 8 papers, 7 papers with code

LogicPrpBank: A Corpus for Logical Implication and Equivalence

1 code implementation14 Feb 2024 Zhexiong Liu, Jing Zhang, Jiaying Lu, Wenjing Ma, Joyce C Ho

Logic reasoning has been critically needed in problem-solving and decision-making.

Decision Making

Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining

1 code implementation15 Oct 2023 Zhexiong Liu, Mohamed Elaraby, Yang Zhong, Diane Litman

This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023.

Argument Mining Classification +2

Ticket-BERT: Labeling Incident Management Tickets with Language Models

no code implementations30 Jun 2023 Zhexiong Liu, Cris Benge, Siduo Jiang

An essential aspect of prioritizing incident tickets for resolution is efficiently labeling tickets with fine-grained categories.

Active Learning Language Modelling +1

Predicting the Quality of Revisions in Argumentative Writing

1 code implementation1 Jun 2023 Zhexiong Liu, Diane Litman, Elaine Wang, Lindsay Matsumura, Richard Correnti

The ability to revise in response to feedback is critical to students' writing success.


Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

1 code implementation10 Dec 2022 Zhexiong Liu, Licheng Liu, Yiqun Xie, Zhenong Jin, Xiaowei Jia

One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks.


ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining

1 code implementation ArgMining (ACL) 2022 Zhexiong Liu, Meiqi Guo, Yue Dai, Diane Litman

The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e. g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective.

Argument Mining Persuasiveness

Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays

1 code implementation25 Jun 2022 Ke Yu, Shantanu Ghosh, Zhexiong Liu, Christopher Deible, Kayhan Batmanghelich

The critical component in our framework is an anatomy-guided attention module that aids the downstream observation network in focusing on the relevant anatomical regions generated by the anatomy network.

Anatomy Anomaly Detection

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

2 code implementations6 Dec 2021 Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Shrivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, Michael A. Yee, Jing Zhang, Yue Zhang

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.

Data Augmentation

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