Visualizing and Understanding Deep Learning Models in NLP
演讲者： Jiwei Li
Abstract: A long-standing criticism of neural network models is their lack of interpretability: results for neural models are hard to interpret.In this talk, I will discuss a few attempts trying to rationalize outputs from neural networks. Methods include simply calculating first-order gradients, computing the difference in log likelihood on gold-standard labels when some words are erased, and a more sophisticated model that uses a reinforcement learning model to find the minimal set of words that must be erased to change the model’s decision.
References:Understanding Neural Networks through Representation Erasure: https://arxiv.org/pdf/1612.08220.pdf
Visualizing and Understanding Neural Models in NLP: https://arxiv.org/pdf/1506.01066.pdf
Bio: Jiwei Li got his B.S. in Biology from Peking University (2008-2012) and Ph.D in Computer Science from Stanford University (2014-2017). He was a winner of Facebook Fellowship 2015 and Baidu Fellowship 2016. He works on Natural Language Processing, advised by Prof. Dan Jurafsky.