Papers by Woojeong Jin
Analyzing Norm Violations in Live-Stream Chat (2023.emnlp-main)
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Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park
| Challenge: | Existing methods for detecting toxic language and norm violations are limited to live-streaming platforms . existing methods are less effective when applied to live streaming platforms based on a limited time frame . |
| Approach: | They propose to use contextual information to automatically moderate toxic content on live streaming platforms. |
| Outcome: | The proposed model improves on live-streaming platforms by 35%. |
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer (2022.acl-long)
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| Challenge: | Pre-trained language models lack visual knowledge of common objects due to reporting bias. |
| Approach: | They investigate whether integrating visual knowledge into a language model can fill the gap . they use captions and images to transfer visual knowledge to 5 downstream tasks . |
| Outcome: | The proposed model can improve performance on 5 tasks that may need visual knowledge to solve the problem. |
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning (2023.emnlp-main)
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| Challenge: | Temporal knowledge graphs (TKGs) are used to represent real-world facts in a structured way. |
| Approach: | They propose to use in-context learning with large language models for TKG forecasting . they compare naive LLMs to state-of-the-art (SOTA) supervised models . |
| Outcome: | The proposed approach performs well against pre-trained large language models . the proposed approach is based on simple heuristics and state-of-the-art models compared with pre-trainers . |
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models (2022.acl-long)
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| Challenge: | Recent few-shot learning models such as GPT3 are expensive and slow to deploy for real-world applications. |
| Approach: | They propose a prompt-based low-resource learning method for VL tasks with a few examples . they pre-train a sequence-to-sequence transformer model with prefix and masked language modeling . |
| Outcome: | The proposed method outperforms Frozen on vision-language tasks with prompt-based learning by 18.2% point. |
Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs (2020.emnlp-main)
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| Challenge: | Existing methods for reasoning over temporal knowledge graphs focus on past timestamps and are not able to predict future interactions. |
| Approach: | They propose a novel autoregressive architecture for predicting future interactions using a recurrent event encoder and a neighborhood aggregator. |
| Outcome: | The proposed method achieves state-of-the-art on five public datasets. |
Collaborative Policy Learning for Open Knowledge Graph Reasoning (D19-1)
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| Challenge: | Existing models of knowledge graph reasoning suffer from limited performance when working on sparse and incomplete graphs due to the lack of evidential paths that can reach target entities. |
| Approach: | They propose a framework to train two collaborative agents to reason for missing facts over a graph augmented by a text corpus. |
| Outcome: | Experiments on two public datasets show the proposed approach is effective on a knowledge graph reasoning task. |
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding (2021.findings-emnlp)
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| Challenge: | Current knowledge distillation models are limited and lack performance on multimodal datasets. |
| Approach: | They propose a multimodal knowledge distillation framework to transfer knowledge from a teacher on multimodal tasks by learning the teacher's behavior within each modality. |
| Outcome: | The proposed framework achieves better performance than KD on four multimodal datasets. |
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data (2021.acl-long)
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| Challenge: | Existing automated forecasting studies rely on structured data to predict future events. |
| Approach: | They propose a question-answering task that limits access to unstructured text data . they use a crowdsourced dataset to form a restricted-domain, multiple-choice, question-announcement task . |
| Outcome: | The proposed model achieves 61.0% accuracy on the dataset, which still lags behind human performance by about 19%. |