The readings for this section of the course have been selected with several criteria in mind --- in no particular order, inspirational-ness, variety in topic and author lists, accessibility to those without an NLP background, availability or reproducibility of datasets; brevity; recency.
These assignments are not paper reviews, so don't include (irrelevant) paper summaries or criticisms. The intent is to use these papers as springboards for potential future work (yours or your classmates'); the papers have already been reviewed, so let's not review them again!
The two-part task for each assignment:
Individually post to the course discussion site a roughly three-paragraph project proposal inspired by one of the readings listed for relevant lecture. You do not have to carefully read both papers for a given assignment, but I expect that in order to choose one of them, you might skim them both.
In your proposal, include the general idea, at least one specific research question, and a suggestion for a dataset. If you have ideas of what techniques/methods you'd like to try, mention those as well.
Things to think about while reading the papers:
Assessment criteria: Proposals: thoughtfulness and creativity are most important to me, but also take feasibility into account. Feedback: thoughtfulness and creativity of your feedback to others.
Academic Integrity Academic and scientific integrity compels one to properly attribute to others any work, ideas, or phrasing that one did not create oneself. To do otherwise is fraud.
Certain points deserve emphasis here. In this class, talking to and helping others is strongly encouraged. You may also, with attribution, use the code from other sources. The easiest rule of thumb is, acknowledge the work and contributions and ideas and words and wordings of others. Do not copy or slightly reword portions of papers, Wikipedia articles, textbooks, other students' work, Stack Overflow answers, something you heard from a talk or a conversation or saw on the Internet, or anything else, really, without acknowledging your sources. See "Acknowledging the Work of Others" in The Essential Guide to Academic Integrity at Cornell and http://www.theuniversityfaculty.cornell.edu/AcadInteg/ for more information and useful examples.
This is not to say that you can receive course credit for work that is not your own — e.g., taking someone else's report and putting your name at the top, next to the other person(s)' names. However, violations of academic integrity (e.g., fraud) undergo the academic-integrity hearing process on top of any grade penalties imposed, whereas not following the rules of the assignment “only” risks grade penalties.