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Language, Information, and Learning at Yale (LILY)

This is the website for the LILY (Language, Information, and Learning at Yale) Lab at the Department of Computer Science, Yale University.

NEWS

  • Apr. 2021 Our SummEval repository is online and pip-installable now. Check it out!
  • Apr. 2021 One paper published at npj digital medicine: COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization!
  • Apr. 2021 Our new dataset FeTaQA: Free-form Table Question Answering has been released online now! Check it out!
  • Apr. 2021 We updated aan.how, which now has over 16k manually-curated resources on NLP and related topics!
  • Apr. 2021 Khera Awarded Career Development Grant from National Heart, Lung, and Blood Institute!
  • Mar. 2021 Three papers accepted to NAACL! Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation, DART: Open-Domain Structured Data Record to Text Generation, and QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization!
  • Mar. 2021 Two papers accepted to ICLR! GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing and SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing!
  • Mar. 2021 Tao Yu has successfully defended his PhD dissertation on semantic parsing for natural language interfaces. Congratulations to Dr. Yu!
  • Feb. 2021 Alex Fabbri has successfully defended his PhD dissertation on natural language processing for text summarization. Congratulations to Dr. Fabbri!

The LILY Lab started in Spring 2017 with Professor Dragomir Radev joining Yale University. Our interests include:

  • Natural Language Processing
    • Information Retrieval
    • Summarization
    • Multilingual Parsing
    • Dialogue Systems
    • Question Answering
    • Humor Detection
  • Machine Learning
    • Neural Networks
    • Semi-supervised Learning