CS 690N, Spring 2017

Final Project

See also the 2/23 project slides and project resources page.


The final project is to either build a natural language processing system, or apply one for some task. The project must use or develop a dataset, and report empirical results or analyses with the dataset. It may use machine learning or rule-based approaches. It may use any type of open-source or widely available software.

You can choose to emphasize:

Different projects will have different balances of these three things.

This course does not have a final exam. The final project is the focus for the final part of the course.

The project will be completed in groups of 1-3. We encourage size 2, which often works well.

The project has four components over the second half of the semester: Proposal, Progress Report, Presentation, and Final report.

(Requirements for the items after the proposal are subject to revision as we get closer to them.)

Proposal (due 3/10)

A 2-4 page document outlining the problem, your approach, possible dataset(s) and/or software systems to use. This proposal

In general, you should illustrate that you have learned about and thought through some of the problem space and possible avenues of analysis and approaches to the problem.

Ideally, try to answer the following questions as well.

Formatting: please use a 10 to 12 point font with single spacing.

Groups: All group members must submit a copy on Gradescope. Clearly list all group members on the document. Gradescope should let you mark group members.

In special cases, some groups may want to change after this point. That's OK, but please be very clear when doing later turn-ins.

Progress Report (due 4/7)

You’ve had a few weeks to work on the project! You have now clarified and revised your proposed idea. You have started working on it and have some preliminary results to report.

The progress report is a 5-10 page document that describes your preliminary work and results. You should do and report on work including

Poster session presentations (5/4)

We will have a poster session where all groups will present their work. It will be open to the community, in conjunction with the Data Science Tea. It should be fun!

Final Report (due 5/11 at end of semester)

The final report is a 12 to 20 page document that describes your project and final results. Unlike the proposal (which was only about a possible project and related work), or the progress report (which was only about results), the final report must be a complete, standalone document. Conceptually, it should include the content of both the proposal and progress report, though they will be changed. The final report describes and motivates the problem, places it in context of related work, describes the dataset and your approach, and reports results with discussion and thoughts for future work.

Submit your PDF on Moodle. Also submit a zip file with your implementation code. (Moodle limits the size of the zip file, so don’t include large data files, but feel free to provide us a URL to them.)

Here is a sample outline for your final report. There are different possible ways to structure it (for example, if you can, you can weave related work into the other sections), but we suggest you follow this outline unless you have substantial prior experience writing technical reports and research papers.

Some things to remember:

Writing a paper is like composing a piece of art. Be deliberate in your choice of what to include, when to include it and how to express it. For example, don’t include every plot you generated; pick the ones that best demonstrate your results. Work on the clarity of your writing. At the end of the day, your report is all the reader has to generate an opinion about your work. You don’t want good work to be obscured by poor writing.

Formatting: write using the ACL stylesheet. Please divide your report clearly into sections/subsections.