Questions for understanding a paper

This is a set of general questions that helps me to get involved in reading a paper.

 

Basic information

Q) Bibliography

Tran, Van-Khanh, and Le-Minh Nguyen. "Dual latent variable model for low-resource natural language generation in dialogue systems." arXiv preprint arXiv:1811.04164 (2018).

 

Q) Link 

 

Q) Cited by

Motivation

Q) What is the domain this paper is in?

 

Q) What is the desired task?

 

Q) Suggest the running example. What is the main point of the task?

 

Q) What is the limitation of the previous works?

 

Q) What is the objective of this paper? (to review / to prove / to supplement / to show)

 

Q) What is the term this work is called (suggest abbreviation, if exists)?

 

Q) What is the main figure? What is the main point of it?

 

Q) What is the contribution of this paper?

 

 

Background

Q) What kind of attempts was there to solve the same problem?

 

Q) What is the limitation of the previous works?

 

Q) Which paper is the most similar one? Why is that?

 

Q) What point is the difference between that similar work and this paper?

 

Model

Q) Which ML model did they use?

 

Q) How many parameters are there?

 

Q) How much was the training cost? What facilities did the authors use?

 

Q) Describe the algorithm.

 

Q) Suggest the main formulation.

 

Q) What are the limitations of the model?

 

Q) Is the code for replication available?

 

Q) What are the baseline models?

 

 

Dataset

Q) Which dataset is used?

 

Q) How large is the dataset (w.r.t. MB and w.r.t. the number of elements)?

 

Q) Is the dataset available in public? If yes, where can we get that?

 

Q) Suggest the sample data?

Results

Q) What kind of metrics did they use?

 

Q) How good is the result?

 

Q) Are those metrics reasonable for this work? why is that?

 

Q) What is the limitation of the results?

 

Further Questions

Q) Has the algorithm been applied to any (NLP, vision, speech) application?

 

Q) If so, what are the tasks that the algorithm is applied to learn from?

 

Q) Is any change to the algorithm needed for the (NLP, vision, speech) application?

 

Q) Is this the only way to solve the problem?

 

Q) Is the work applicable to the Question Answering task?

 

------------------------------ Example ------------------------------

 

Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems