CSI5180: Guidelines for the Final Report
Due Date: Last Day of Classes
Page Limits: n <= 25 (single space)
Here is a set of guidelines which, I hope, will assist you in the
writing of your final project report. As a general rule, your report
should be understandable by anyone with a reasonable understanding of
machine learning but who doesn't know the particular approaches or the data
that you used. As well, as you write, you should try to imagine that
you are conversing with a very interactive reader (who doesn't know
anything about your project but who wants to find out everything!).
Try to be this reader and to guess all the questions s/he would ask
you and all the challenges s/he would have for you. Then incorporate
your answers to these questions and challenges in your report so that,
hopefully, you will have pre-empted many of your (real) readers' questions.
In addition to the introduction and conclusion (which can be thought of
as summaries of your study directed at a general audience, but with more
emphasis on your motivations in the case of the introduction and more
emphasis on your results and their implications in the case of the
conclusion), your report should contain:
- A statement of the problem you are studying.
- A review of the related literature on the topic and a discussion of
where your study fits in this previous literature.
- A description of the method you have designed or of the methods
you are comparing. Assume that the reader does not know how the systems
you have designed and/or used work.
- A description of the data to which you applied your research
- A description of the methodology you used to set the various
parameters of the systems you used. The idea here is that all your
results should be reproduceable by anyone reading your paper.
- A description of your evaluation methodology and a discussion of why
this evaluation methodology is appropriate. [Note: coming up with a
reliable evaluation strategy is very difficult in NLP.] You have to
think very carefully about how you will demonstrate the usefulness of
your system or study long before you even start building your system
or designing your experiments. E.g., Will you use human evaluation?
Do you have several human subjects willing to evaluate your approach?
If you don't use human evaluation, what test can you propose that
will support your claims?
- A description of your results. Think of the format that would best
illustrate the points you are trying to make. Should you list your results in
a table? represent them with a graph? what sort of graph? what results are
necessary to report?
- A discussion of your results. i.e., a section that explains why, in your
opinion, the results you reported were obtained: why the system you
designed or approaches you compared were successful or why they failed.
If you want, you can also
discuss what you think would happen under conditions different from those
you specifically tested.
- A discussion of the relevance of your results: what have you achieved
with your study? How do your results support the claims you have made
in the earlier parts of your report?
- A section discussing future work. There, you should try to identify sets
of experiments that would be interesting to run and to discuss why
they would be interesting (i.e., what are the issues that such experiments