Slide 7
Slide 7 text
Attention Mechanisms in NLP
7
Attention mechanisms [Bahdanau+’14]
● learn conditional distributions over input units
to compose a weighted context vector
● significantly contribute to improving
the performance of NLP tasks e.g., text
classification [Lin+’17], question answering
[Golub+’16], natural language inference [Parikh+’16]
Image from Bahdanau+’14
Interpretability through the mechanisms
● Attention weights are often claimed to afford
insights into the “inner-workings” of models
➜ “Attention provides an important way to
explain the workings of neural models” [Li+’16]
● The claims that attention provides interpretability are common
in the literature [Xu+’15, Choi+’16, Xie+’17, Lin+’17]
Attention heatmap of Yelp
reviews with 5 star review
Image from Lin+’17
However, it has been pointed out that DNN models tend
to be locally unstable, and even tiny perturbations to the
original inputs [Szegedy+’13] or attention mechanisms [Jain+’19]
can mislead the models.
➜ Maliciously perturbations are called
adversarial examples or adversarial perturbations
Introduction > Contribution > Basemodel > Methods > Experiments > Conclusion