[AI Seminar] AI Seminar sponsored by Apple -- Adams Wei Yu -- May 08

[Adams Wei Yu]

Dear faculty and students,

We look forward to seeing you next Tuesday, May 08, at noon in *GHC 6115* for
AI Seminar sponsored by Apple. To learn more about the seminar series,
please visit the AI Seminar webpage <http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday, Adams Wei Yu <http://www.cs.cmu.edu/~weiyu/> will give the
following talk:

Title: Efficient and Effective Models for Machine Reading Comprehension

Abstract:

Machine reading comprehension has attracted lots of attentions in the AI,
ML and NLP communities. In this talk, I will introduce two efficient and
effective models to approach this task.

Firstly, I will propose a model, LSTM-Jump, that can skip unimportant
information in sequential data, mimicking the skimming behavior of human
reading. Trained with an efficient reinforcement learning algorithm, this
model can be several times faster than a vanilla LSTM in inference time.

Then I will introduce a sequence encoding method that discards recurrent
networks, which thus fully supports parallel training and inference. Based
on this technique, a new question-answering model, QANet, is proposed.
Combined with data augmentation approach via backtranslation, this model
achieves No.1 performance in the competitive Stanford Question and Answer
Dataset (SQuAD), while being times faster than the prevalent models.
Notably, the exact match score of QANet has exceeded human performance.

The talk is based on the following two works:
1. http://aclweb.org/anthology/P17-1172
2. https://arxiv.org/pdf/1804.09541.pdf
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