Natural Language Processing and Social Interaction, Fall 2019 (original) (raw)

Prerequisites All of the following: CS 2110 or equivalent programming experience (Python encouraged); a course in artificial intelligence or any relevant subfield (e.g., NLP, information retrieval, machine learning, Cornell CS courses numbered 47xx or 67xx); proficiency with using machine learning tools (e.g., fluency at training an SVM, comfort with assessing a classifier’s performance using cross-validation)

Enrollment Limited to [[PhD and [CS MS] students] who meet the prerequisites]. If you are interested in taking the class but do not belong to these categories, come to first day of class when enrolment will be discussed. Auditing (either officially or unofficially) is not permitted.

Related classes: see Cornell's NLP course list

The homepage for the previous running of CS6742 may also be useful. Here is the list of all prior runnings: 2017 fall :: 2016 fall :: 2015 fall :: 2014 fall :: 2013 fall:: 2011 spring

Administrative info and overall course structure

Course homepage http://www.cs.cornell.edu/courses/cs6742/2019fa. Main site for course info, assignments, readings, lecture references, etc.; updated frequently.

CMS page http://cms.csuglab.cornell.edu. Site for submitting assignments, unless otherwise noted.

Piazza page http://piazza.com/cornell/Fall2019/cs6742 Course announcements and Q&A/discussion site. Social interaction and all that, you know. (Access code provided on first day of classes.)

Contacting the instructor

Overview of course schedule. Details subject to change. Full schedule is maintained on the main course webpage.

Lecture Agenda Pedagogical purpose Assignments
#1 Course overview A1: Pilot empirical study for a research idea based on provided datasets and readings.
# 2 - #3 Get-to-know-you exercises to get everyone familiar and comfortable with each other. A1 related discussions. How to form research questions and quickly test their feasibility.
# 4 - #7 Lecture topics related to the A1 startup projects: Conversational Structure, Lingusitic Cues, Conversation-specific Phenomena. Case studies to explore some topics and research styles find interesting.
Next block of meetings Dicussion of proposed projects based on starter projects and on topical readings Practice with fast research-idea generation. Feedback as to what proposals are most interesting, most feasible, etc. Discussion of student project proposals, based on the readings for that class meeting. Each class meeting thus involves everyone reading at least one of the two assigned papers and posting a new research proposal based on the reading to Piazza. Thoughtfulness and creativity are most important to , but take feasibility into account.
Next block of meetings Lectures on, potentially, linguistic socialization, conversational failure, moderation, influence, persuasion, diffusion, discourse structure, advanced language modeling Familiarity with foundational material: concepts and methodology. Potentially some assignments based on the lectures.
Remainder of the course Activities related to course projects Development of a "full-blown" research project (although time restrictions may limit ambitions). For our purposes, "interesting" is more important than "thorough".
Some time in December (to be determined by the registrar): final project writeup due

Grading Of most interest to is productive research-oriented discussion participation (in class and on Piazza), interesting research proposals and pilot studies, and a good-faith final research project.

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.

We emphasize certain points 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 http://www.cs.cornell.edu/courses/cs6742/2011sp/handouts/ack-others.pdf 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 risk grade penalties.