Time (EST)	Session I (Morning)	Speakers
09:00-09:15 am	Session: Opening Remarks	Meng Jiang
09:15-10:00 am	Keynote talk 1: From NLP to NLU: Why we need varied, comprehensive, and stratified knowledge, and how to use it for Neuro-symbolic AI [slides]	Amit Sheth
10:00-10:45 am	Keynote talk 2: Deep Learning for Code Understanding and Generation: Challenges and Opportunities [slides]	Chandan Reddy
10:45-11:30 am	Keynote talk 3: Raw Knowledge vs. Understanding: What Adversarial QA Reveals about the Limits of AI	Jordan Boyd-Graber
11:30-12:00 pm	Oral presentations 1 Talk 1: DRAGON: Deep Bidirectional Language-Knowledge Graph Pretraining. Michihiro Yasunaga et. al. [paper] Talk 2: ComFact: A Benchmark for Linking Contextual Commonsense Knowledge. Silin Gao et. al. [paper]
Time (EST)	Session II (Afternoon)	Speakers
13:30-14:15 pm	Keynote talk 4: Efficient & Scalable NLP through Retrieval-Augmented Language Models [slides]	Scott Yih
14:15-15:15 pm	Oral presentations 2 Talk 3: Knowledge Retrieval Over Public and Private Data. Simran Arora et. al. [paper] Talk 4: Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering. Pan Lu et. al. [paper] Talk 5: Knowledge Relevance BERT: Integrating Noisy Knowledge into Language Representation. Karan Samel et. al. [paper] Talk 6: Improving Mental Health Support Response Generation with Eventuality-based Knowledge Graph. Lingbo Tong et. al. [paper]
15:15-16:15 pm	Coffee break and Poster Session
16:15-17:15 pm	Panel discussion Topic: Knowledge, NLP and LLM	Rachel Rudinger, Niket Tandon, Rui Zhang, Emily Ching, Thomas Pellissier Tanon
17:15-17:30 pm	Final Remarks	Chenguang Zhu