[AI Seminar] AI Seminar sponsored by Apple -- Qizhe Xie -- Oct. 9th

[Han Zhao]

Dear faculty and students:

We look forward to seeing you next Tuesday, Oct. 9th, at noon in GHC 6115
for AI Seminar sponsored by Apple. To learn more about the seminar series,
please visit the website.
On Tuesday, Qizhe Xie will give the following talk:

Title: From Credit Assignment to Entropy Regularization: Two New Algorithms
for Neural Sequence Prediction

Background:
Modeling and predicting discrete sequences is the central problem to many
natural language processing tasks. Despite the distinct evaluation metrics
for different tasks, the standard training algorithm for language
generation has been maximum likelihood estimation (MLE). However, the MLE
algorithm has two obvious weaknesses: (1) the MLE training ignores the
information of the task specific metric; (2) MLE can suffer from the
exposure bias, which refers to the phenomenon that the model is never
exposed to its own failures during training. The recently proposed reward
augmented maximum likelihood (RAML) tackles these problems by constructing
a task metric dependent target distribution, and training the model to
match this task-specific target instead of the empirical data distribution.

Abstract:
In this talk, we study the credit assignment problem in reward augmented
maximum likelihood (RAML), and establish a theoretical equivalence between
the token-level counterpart of RAML and the entropy regularized
reinforcement learning. Inspired by the connection, we propose two sequence
prediction algorithms, one extending RAML with fine-grained credit
assignment and the other improving Actor-Critic with a systematic entropy
regularization. On two benchmark datasets, we show that the proposed
algorithms outperform RAML and Actor-Critic respectively.

-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
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