- The attribute-based approach can be useful if the attribute is given. This limits the application of the proposed approach if there is no attribute given but the text is implicitly offensive.
- It is not described if the knowledge bases that are inserted in are free from societal biases, or the issue is not affected by such restriction.
Comments - I like attacking implicit offensive texts with reasoning chains, but not yet convinced with the example of Fig. 1. If other contexts such as 'S1 is fat/poor' is not given, then the conversation between S1 and S2 seems quite natural. The addressee may ask if bookclubs provide free food without offensive intention. If the word of S2 is to be decided as 'implicitly offensive', then one of the reasoning chain, such as 'you are fat/poor', should be provided as a context. If I correctly understood, I recommend the authors to change the example to more clear-cut and non-ambiguous one.
- I found some issues in the provided example; i) AIR is still written as ASR, and ii) there are some empty chains in the full file. Hope this to be checked in the final release.
Suggestions - It would be nice if the boundary between explicit and implicit offensive texts is stated clearly. Is it decided as explicit if the statement contains specific terms and offensive? Or specific expression / sentence structure?
- Please be more specific on the 'Chain of Reasoning' section, especially line 276.
- Please describe more on MNLI corpus, on the reason why the dataset is utilized in the training of entailment system.
Typos - TweetEval << two 'L's in line 349
- It is not described if the knowledge bases that are inserted in are free from societal biases, or the issue is not affected by such restriction. Comments - I like attacking implicit offensive texts with reasoning chains, but not yet convinced with the example of Fig.