Venue: ICLR 2018 conference

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: my main concern is the relevance of this paper to ICLR.
This paper is much related not to representation learning but to user-interface.
The paper is NOT well organized and so the technical novelty of the method is unclear.
For example, the existing method and proposed method seems to be mixed in Section 2.
You should clearly divide the existing study and your work. 
The experimental setting is also unclear.
KSS seems to need the user study.
But I do not catch the details of the user study, e.g., the number of users.

review_title: The relevance of this paper to ICLR is low and the details of the experiments are missing.

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper deals with improving language models on mobile equipments
based on small portion of text that the user has ever input. For this
purpose, authors employed a linearly interpolated objectives between user
specific text and general English, and investigated which method (learning
without forgetting and random reheasal) and which interepolation works better.
Moreover, authors also look into privacy analysis to guarantee some level of
differential privacy is preserved.

Basically the motivation and method is good, the drawback of this paper is
its narrow scope and lack of necessary explanations. Reading the paper,
many questions arise in mind:

- The paper implicitly assumes that the statistics from all the users must
  be collected to improve "general English". Why is this necessary? Why not
  just using better enough basic English and the text of the target user?

- To achieve the goal above, huge data (not the "portion of the general English") should be communicated over the network. Is this really worth doing? If only
  "the portion of" general English must be communicated, why is it validated?

- For measuring performance, authors employ keystroke saving rate. For the
  purpose of mobile input, this is ok: but the use of language models will
  cover much different situation where keystrokes are not necessarily 
  available, such as speech recognition or machine translation. Since this 
  paper is concerned with a general methodology of language modeling, 
  perplexity improvement (or other criteria generally applicable) is also
  important.

- There are huge number of previous work on context dependent language models,
  let alone a mixture of general English and specific models. Are there any
  comparison with these previous efforts?

Finally, this research only relates to ICLR in that the language model employed
is LSTM: in other aspects, it easily and better fit to ordinary NLP conferences, such as EMNLP, NAACL or so. I would like to advise the authors to submit
this work to such conferences where it will be reviewed by more NLP experts.

Minor:
- t of $G_t$ in page 2 is not defined so far.
- What is "gr" in Section 2.2?

review_title: More explanations on the assumption are required

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper discusses the application of word prediction for software keyboards. The goal is to customize the predictions for each user to account for member specific information while adhering to the strict compute constraints and privacy requirements. 

The authors propose a simple method of mixing the global model with user specific data. Collecting the user specific models and averaging them to form the next global model. 

The proposal is practical. However, I am not convinced that this is novel enough for publication at ICLR. 

One major question. The authors assume that the global model will depict general english. However, it is not necessary that the population of users will adhere to general English and hence the averaged model at the next time step t+1 might be significantly different from general English. It is not clear to me as how this mechanism guarantees that it will not over-fit or that there will be no catastrophic forgetting.
review_title: Very relevant for the application in mind. Too simple for publication.

Meta-review: The committee feels that this paper presents a simple, yet effective way to adapt language models from various users in a sufficiently privacy preserving way.  Empirical results are quite strong.  Reviewer 3 says that the novelty of the paper is not great, but does not provide any references to prior work that are similar to this paper.  The meta-reviewer finds the responses to Reviewer 3 sufficient to address the concerns.

Similarly, Reviewer 2 says that the paper may not be relevant to ICLR, but the committee feels its content does belong to the conference since the topic is extremely relevant to modern language processing techniques.  In fact, the authors provide several references that show that this paper is similar in content to those submissions.

Reviewer 1's concerns are also not sufficiently strong to warrant rejection.  The responses to each criticism suffices and the meta-reviewer thinks that this paper will add value to the conference.
Decision: Accept (Poster)