no code implementations • EMNLP 2020 • Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren
The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.
no code implementations • NAACL (maiworkshop) 2021 • Woojeong Jin, Maziar Sanjabi, Shaoliang Nie, Liang Tan, Xiang Ren, Hamed Firooz
In this paper, we propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets.
no code implementations • 21 Feb 2024 • Woojeong Jin, Tejas Srinivasan, Jesse Thomason, Xiang Ren
We present WinoViz, a text-only evaluation dataset, consisting of 1, 380 examples that probe the reasoning abilities of language models regarding variant visual properties of objects under different contexts or states.
no code implementations • 24 May 2023 • Woojeong Jin, Subhabrata Mukherjee, Yu Cheng, Yelong Shen, Weizhu Chen, Ahmed Hassan Awadallah, Damien Jose, Xiang Ren
Generalization to unseen tasks is an important ability for few-shot learners to achieve better zero-/few-shot performance on diverse tasks.
no code implementations • 18 May 2023 • Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Young Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park
Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter.
1 code implementation • 17 May 2023 • Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, Jay Pujara
This shows that prior semantic knowledge is unnecessary; instead, LLMs can leverage the existing patterns in the context to achieve such performance.
no code implementations • ACL 2022 • Woojeong Jin, Dong-Ho Lee, Chenguang Zhu, Jay Pujara, Xiang Ren
Pre-trained language models are still far from human performance in tasks that need understanding of properties (e. g. appearance, measurable quantity) and affordances of everyday objects in the real world since the text lacks such information due to reporting bias.
1 code implementation • ACL 2022 • Woojeong Jin, Yu Cheng, Yelong Shen, Weizhu Chen, Xiang Ren
Large pre-trained vision-language (VL) models can learn a new task with a handful of examples and generalize to a new task without fine-tuning.
Ranked #4 on Image Captioning on Flickr30k Captions test (SPICE metric)
no code implementations • Findings (EMNLP) 2021 • Woojeong Jin, Maziar Sanjabi, Shaoliang Nie, Liang Tan, Xiang Ren, Hamed Firooz
The idea aims at mimicking a teacher's modality-specific predictions by introducing auxiliary loss terms for each modality.
no code implementations • ACL 2021 • Woojeong Jin, Rahul Khanna, Suji Kim, Dong-Ho Lee, Fred Morstatter, Aram Galstyan, Xiang Ren
In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data.
1 code implementation • 9 Mar 2020 • Sankalp Garg, Navodita Sharma, Woojeong Jin, Xiang Ren
We show that if the information contained in the graph and the time series data are closely related, then this inter-dependence can be used to predict the time series with improved accuracy.
no code implementations • 25 Sep 2019 • Woojeong Jin, He Jiang, Meng Qu, Tong Chen, Changlin Zhang, Pedro Szekely, Xiang Ren
We present Recurrent Event Network (RE-Net), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e. g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events.
2 code implementations • IJCNLP 2019 • Cong Fu, Tong Chen, Meng Qu, Woojeong Jin, Xiang Ren
We propose a novel reinforcement learning framework to train two collaborative agents jointly, i. e., a multi-hop graph reasoner and a fact extractor.
2 code implementations • 11 Apr 2019 • Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren
The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.