Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper proposes to do short-text classification by training a character-level RNN language model on each class, and to then use the error rates of each language model in predicting the next character in a test string as a source of features for use when classifying that string.

Pros:
– Empirical results on abusive tweet detection are strong. 

Cons:
– It's not clear why this technique works, and with an evaluation on only one somewhat marginal NLP task, it's hard to know if when (if ever) this technique is worth using in practice.
– This work seems a bit too narrow in scope to count as exciting late-breaking work. I think this idea would be best presented as a short paper at an NLP workshop, or fleshed out on a broader range of tasks, analyzed a bit, and presented as a regular long paper.

Questions:
– High-level: Could you comment on how this fits with the stated goals of the workshop?
– Detail: If I understand correctly, the input words are embedded, and the embeddings are then fed through a PReLU before being used in the class-conditional GRU RNNs. This seems like an odd use for a nonlinearity—did adding the PReLU help?


review_title: Borderline

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper proposes to use character-level language model errors as features for text categorization tasks. While the paper is well-written and the experiments show gains over baseline methods on an abusive language detection task, I don't think this paper fits within the goals of the workshop: it does not contain anything novel on the ML side, and the task is more of interest to the NLP community. I hope the authors can apply their method to different tasks in future versions of the paper, and also try to better motivate their architecture design decisions.

pros:
- good result on abusive language detection task compared to baseline models
- the paper is well-written, it is clear what the task is and how the model architecture is designed

cons:
- not clear why you need a language model for each class, how does this compare to just training a single LM over the entire dataset and using its error as a feature?
- how does this method compare to just counting the number of unknown words per example and using that as a feature? i don't think the current experiments compare the proposed method against appropriate baselines, you need baselines that take into account OOV words
- the "instance normalization" is not well-motivated, why use it over just the raw error vector? and why further pass it through another neural network? 
- the task is very specific and not a standard text categorization problem. it would be nice to demonstrate the method's effectiveness on a variety of well-established benchmark tasks.
review_title: review

Decision: Reject