Introduction In this blog post, we introduce our AAAI 2019 accepted paper “What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning.” Our LectureBank dataset contains 1,352 English lecture files collected from university courses in mainly Natural Language Processing (NLP) field. Besides, each file is manually classified according to an existingContinue reading “LectureBank: a dataset for NLP Education and Prerequisite Chain Learning”
Author Archives: Irene
ELMo in Practice
ELMo: Deep contextualized word representations In this blog, I show a demo of how to use pre-trained ELMo embeddings, and how to train your own embeddings.
Slideshare (4): A brief Introduction on Transfer Learning
Please check my notes for Transfer Learning introduction! Transfer Learning
Transfer Learning Materials
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Slideshare (3): Unsupervised Transfer Learning Methods
A brief introduction on unsupervised transfer learning methods. The presentation focused on unsupervised transfer learning methods, introducing feature-based and model-based strategies and few recent papers from ICML, ACL. Unsupervised Transfer Learning Comments are welcomed!
TensorFlow 08: save and restore a subset of variables
TensorFlow provides save and restore functions for us to save and re-use the model parameters. If you have a trained VGG model, for example, it will be helpful for you to restore the first few layers then apply them in your own networks. This may raise a problem, how do we restore a subset ofContinue reading “TensorFlow 08: save and restore a subset of variables”
To copy or not, that is the question: copying mechanism
In our daily life, we always repeating something mentioned before in our dialogue, like the name of people or organizations. “Hi, my name is Pikachu”, “Hi, Pikachu,…” There is a high probability that the word “Pikachu” will not be in the vocabulary extracted from the training data. So in the paper (Incorporating Copying Mechanism inContinue reading “To copy or not, that is the question: copying mechanism”
What matters: attention mechanism
People would be attracted only on a part of an image, say a person on a photo. Similarly, for a given sequence of words, we should pay attention to few keywords instead of treating each word equally. For example, “this is an apple”, when you read it loudly, I am sure you will stress “apple”Continue reading “What matters: attention mechanism”
What’s next: seq2seq models
The short blog contains my notes from Seq2seq Tutorial. Please leave comments if you are interested in this topic.
Deep Learning 16: Understanding Capsule Nets
This post is the learning notes from Prof Hung-Yi Lee‘s lecture, the pdf could be found here (page40-52). I have read few articles, and I found this is a must-read. It is simple, and you can easily understand what is going on. I would say it is a good starting point for further readings. PaperContinue reading “Deep Learning 16: Understanding Capsule Nets”
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