Limitations

Once you complete the analysis, you know what your approach can or cannot do for the task. The limitations can be thought of in multiple dimensions:

• Technicality: what are the technical challenges for enhancing the performance in terms of accuracy or scalability?

• Robustness: how reliable and generalizable are your results?

• Applicability: what hinders your approach from being adapted by practical applications?

Future Directions

As the owner of this work, you are responsible for suggesting future research directions to the community. Explain what researchers can do to overcome the limitations.

Once you have shown the overall performance of your models in the section, you need to give a more detailed analysis.

Individual Writing

• Write the Analysis section in your individual overleaf project.

• Recommended length: 100 - 200 lines (including tables and figures).

• Submit the PDF version of your current draft up to the Analysis section.

Rubric

• Performance Analysis: Is the model performance analyzed in-depth (e.g., by labels and categories)? Is the efficiency of the model illustrated (if applicable)? (5 points)

• Error Analysis: Are the errors meaningfully categorized, interpreted, and illustrated with examples? (3 points)

• Discussions: Are the limitations and future directions clearly described? (2 points)

It is important to understand what errors are produced by your model, how often those errors occur on which occasions, and what causes such errors. Error analysis should be done both quantitatively and qualitatively.

1. Sample instances that your model makes errors for. Focus on data portions that your performance analysis has questions about.

2. Categorize errors by analyzing them (manually) and pick signature examples.

3. Provide distributions of the categorized errors in a figure (see below).

4. Explain why the model makes certian errors with examples.

Contents

• Performance Analysis