
## Spanning the Spectrum of Hatred Detection: A Persian Multi-Label Hate Speech Dataset with Annotator Rationales
Zahra  Delbari, Nafise Sadat Moosavi, Mohammad Taher Pilehvar
Keywords: NLP: Other, NLP: Applications
AAAI/2024/Proceedings/31279 - Spanning the Spectrum of Hatred Detection: A Persian Multi-Label Hate Speech Dataset with Annotator Rationales.pdf

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[7]

The authors use four different LLMs on their presented data set and task. The authors state the implementation is to be found online (https://github.com/Zahra-D/Phate) but code is missing in the link, but they do explain how the models were prompted.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[1]

(1/1)

The authors have published their data on GitHub, with a short description and annotator explanation. 

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[4]

The hyperparameters are given for three models, and seemingly 'default' values are used but this is not stated. No budget regarding the acquisition of the hyperparameters is specified.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The experimental procedure is well explained, including many parameters such as amount of repitions, data splits and evaluation metrics.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[1]

The domain requires some understanding of LLMs and text classification, but is relatively accessible with many examples and anecdotes.
