How well can hate speech concept be abstracted in order to inform automatic classification in codeswitched texts by machine learning classifiers? We explore different representations and empirically evaluate their predictiveness using both conventional and deep learning algorithms in identifying hate speech in a ~48k human-annotated dataset that contain mixed languages, a phenomenon common among multilingual speakers. This paper espouses a novel approach to handle this challenge by introducing a hierarchical approach that employs Latent Dirichlet Allocation to generate topic models that feed into another high-level feature set that we acronym PDC. PDC groups similar meaning words in word families during the preprocessing stage for supervised learning models. The high-level PDC features generated are based on Ombui et al, (2019) hate speech annotation framework that is informed by the triangular theory of hate (Stanberg,2003). Results obtained from frequency-based models using the PDC feature on the annotated dataset of ~48k short messages comprising of tweets generated during the 2012 and 2017 Kenyan presidential elections indicate an improvement on classification accuracy in identifying hate speech as compared to the baseline
Comments: 
-This paper considers codeswitched hate speech texts from an NLP perspective.  The dataset considers mixed languages.  
-Focuses on kenyan presidential election.  
-Paper has severe formatting issues as well as simple issues like capitalization.  Additionally many plots are rather unattractive (seem to be produced using Excel or Google Sheets, whereas generally something like matplotlib or seaborn is preferred).  
  -The paper puts a lot of examples into the main text, whereas these are usually put into the appendix, or only a few examples are placed in the main text.  Usually the main text focuses more on higher level analysis.  
  -Figure 2 is formatted incorrectly (the caption runs on to the next page)
  -I appreciate the effort that went into data annotation as well as the disclosure of the demographics of annotators.  
  -Table 1 should make the metric much clearer (it's mentioned in the main text, but it should be in the caption too, also the best performance usually should be bolded)!  Generally TF-idf features or PDC features seem to have the best performance.  The performance of the CNN does not seem very strong.  I think a simple RNN based approach might also be worth considering.  It would also be worth analyzing if the differences between the methods is attributable to underfitting or overfitting.  
  -If the paper is proposing a new task with many baselines, it's also important to release the dataset and code in my opinion (I believe ICLR allows this to be done in a de-anonymized way).  
Review: 
This paper deals with an important problem in social media analysis.  With the spread of hate speech and hate crimes by rioting separatists in Hong Kong as well as equally hateful attacks on Chinese people in the west, I think that this is an issue that deserves more attention in our community.  Unfortunately this paper needs more polish to be appropriate for ICLR.  It also might be better suited to an NLP focused conference (such as ACL, EMNLP, or NAACL) although I think if the technical contribution is clear enough it could be suitable for ICLR as well.  
I think the big things to focus on would be including more baselines, improving polish in the paper, and providing a clearer high-level analysis of the dataset (with specific examples mostly left for the appendix).