Hi there, I’m Irene Li (李紫辉)! Welcome to my blog! 🙂
I want to share my learning journals, notes and programming exercises with you. The topics include data science, statistics, machine learning, deep learning, AI applications, etc.
I am a second-year Ph.D. student, working in LILY lab at Yale University. My advisor is Prof. Dragomir Radev.
Please leave comments or contact me via irene.li[at]yale.edu.
Recent Research
- Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Alexander Fabbri, Irene Li, Tianwei She, Suyi Li and Dragomir Radev
Proceedings of ACL 2019, Dataset and Code - What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning
Irene Li, Alexander Fabbri, Robert Tung, and Dragomir Radev
Proceedings of AAAI 2019, Dataset BLOG - TutorialBank: Using a Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation
Alexander Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Weitai Ting, Robert Tung, Caitlin Westerfield and Dragomir Radev
Proceedings of ACL 2018 Dataset BLOG
Participated Research
- SParC: Cross-Domain Semantic Parsing in Context
Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher and Dragomir Radev
Proceedings of ACL 2019 - ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks
Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander Fabbri, Irene Li, Dan Friedman, and Dragomir Radev
Proceedings of AAAI 2019 - Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev
Proceedings of EMNLP 2018 BLOG
Earlier Research Works
Recent Posts
- Understanding Variational Graph Auto-Encoders
- Resources for BioNLP: datasets and tools
- Understanding Graph Convolutional Networks
- LectureBank: a dataset for NLP Education and Prerequisite Chain Learning
- ELMo in Practice
- Slideshare (4): A brief Introduction on Transfer Learning
- Transfer Learning Materials
- Slideshare (3): Unsupervised Transfer Learning Methods
- TensorFlow 08: save and restore a subset of variables
- To copy or not, that is the question: copying mechanism
- What matters: attention mechanism
- What’s next: seq2seq models
- Deep Learning 16: Understanding Capsule Nets
- Working with ROUGE 1.5.5 Evaluation Metric in Python
- Reinforcement Learning (1): Q-Learning basics
- Deep Learning 15: Unsupervised learning in DL? Try Autoencoder!
- Slideshare (2): Machine Learning Recap Slides sharing
- TensorFlow 07: Word Embeddings (2) – Loading Pre-trained Vectors
- Deep Learning 14 : Optimization, an Overview
- Deep Learning 13: Understanding Generative Adversarial Network
所以我是第一个
LikeLiked by 1 person
你将名留青史。。。。。如果爱卿点击订阅，朕将甚感欣慰！
LikeLike
我是第二个，猜猜我是谁？
LikeLike
难道你是“朝阳群众”。。。。。。
LikeLike
Good luck
LikeLike
Thanks 🙂
LikeLike
u arr
a wunderbaar 🙂
LikeLike
lol you too
LikeLike
zan ,beautiful lady
LikeLike
We always love you~~
LikeLike
We always love you~~~lol
LikeLike
..见笑了…
LikeLike
So beautiful home page~
LikeLiked by 1 person
Bonjour!
LikeLiked by 1 person
Do you hire interns during summer 2018?
LikeLike
Hi, sorry I think you misunderstood. I mean I myself is looking for an internship job this summer, I am not hiring anyone 😀
Thanks.
LikeLike
A fan of Bach! Great!
LikeLiked by 1 person
Guess who am I
LikeLike
hhh, guess if I can guess who you are
LikeLike