Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 3: Clear rejection
review_content: The paper proposes to train LSTMs to generate call-for-papers conditioned on the topic of the conference. The model is evaluated by comparing the generated calls to the original ones.

As such, this is a rather straight-forward application of LSTM language models. The paper does not justify why this particular application matters. I am not sure how such models could ever help to conference organizers. Besides, comparing the generated calls "on average" with the originals makes little sense: even n-gram models would succeed in such a comparison.

Other issues include:
- the evaluation method is not explained, the paper refers to Latent Semantic Indexing, but there is no explanation in the text what precisely was done
- almost the whole paper is written in the present perfect sense, which does not seem grammatically correct
- \citet should be replace with \citep almost everywhere

To sum up, the paper is not well-motivated, there are issues in both execution and writing. I do not recommend acceptance.
review_title: straight-forward application, poor writing

final_score: rating: 3: Clear rejection
review_content: This paper uses LSTM to generate call for papers by training on topics from an LDA model.
The results are evaluated by measuring the similarity of the generated call for papers to their topic models using Latent Semantic Indexing.

This is an application paper since there it does not propose a new method or analyze existing methods. 
The main contribution is showing that an LSTM can be used to generate call for papers. 
The paper does not elaborate why this particular application is important and/or exciting.
The results are also not convincing, and the evaluation metric is confusing (I do not understand how exactly similarities to topics models are being measured).

review_title: review

Decision: Reject