Based on the review of the README.md file for the ParsiNLU Reading Comprehension task, I've identified the following potential issues:

1. **Issue with Link References**
   - **Issue**: Broken link references in the document.
   - **Evidence**: "The purpose of this task is to measure a pre-trained language model's ability to perform reading comprehension in Persian. This involves finding the span in a given _context paragraph_ that answers a given _question_."
   - **Description**: The document attempts to reference several resources and related pages using relative paths (e.g., `[All tasks](../README.md)` and `[BIG-bench Lite](../keywords_to_tasks.md#big-bench-lite)`), which could lead to broken links depending on the relative location of the files. This might make navigation and cross-referencing difficult for users not accessing the file directly within its GitHub repository structure.

2. **Issue with External Dependency Description**
   - **Issue**: Insufficient explanation regarding the dependency on external tools or scripts.
   - **Evidence**: "This header or footer block was automatically generated by generate_task_headers.py. Do not edit it directly, since it will be automatically overwritten on merge on github."
   - **Description**: The README mentions an automated process using `generate_task_headers.py` but does not provide information on how to access or utilize this script for users who might be interested in contributing or modifying the task. This omission could hinder contributions or edits from users unfamiliar with the project's infrastructure.

3. **Issue with Data Set Description Clarity**
   - **Issue**: Ambiguous description of data filtering.
   - **Evidence**: "Of 570 examples in the original eval set, 52 examples with sensitive topics (e.g., suicide) were filtered for this release, leaving 518 examples."
   - **Description**: While the document specifies that certain examples were removed due to sensitive topics, it does not clearly state the criteria or process used to determine which topics are considered sensitive or how they were identified among the examples. This lack of clarity could raise concerns about the transparency and reproducibility of the dataset preparation.

These issues might affect the usability, understandability, and reproducibility of the dataset and associated tasks.