Lifu Huang

Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer.

2021 · arXiv (Cornell University)

Extracting temporal relations (e.g., before, after, concurrent) among events is crucial to natural language understanding. Previous studies mainly rely on neural networks to learn effective features or manual-crafted linguistic features for temporal relation extraction, which …

How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction

2021 · arXiv (Cornell University)

Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we …

The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

2021 · arXiv (Cornell University)

Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event schema induction focuses either on atomic events or …

AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes

2023 · arXiv (Cornell University)

We propose attribute-aware multimodal entity linking, where the input consists of a mention described with a text paragraph and images, and the goal is to predict the corresponding target entity from a multimodal knowledge base …

BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

2025 · arXiv (Cornell University)

Unifying image understanding and generation has gained growing attention in recent research on multimodal models. Although design choices for image understanding have been extensively studied, the optimal model architecture and training recipe for a unified …

Zero-Shot Transfer Learning for Event Extraction

2018

Most previous supervised event extraction methods have relied on features derived from manual annotations, and thus cannot be applied to new event types without extra annotation effort. We take a fresh look at event extraction …

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

2019

Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.