Methods applicable to a range of problems in NLP, such as
field autoencoders (ADS '14),
linguistic regularizers (YS '14),
alternating directions dual decomposition
retrofitting (FDJDHS '15),
recurrent neural network grammars (DKBS '16), entity language models (JTMCS '17),
scaffolds (STLZDS '18),
rational recurrences (PSTS '18),
deep weighted averaging classifiers (CZS '19), and
knowledge-enhanced contextual word vectors (PNLSJSS '19).
Methodology challenges in NLP, including quantifying artifacts in data (GSLSBS '18),
bias (SZS '19), interpretability (SS '19), and, when models aren't interpretable, other analysis methods (LGBPS '19).
CSE 599D1: Advanced NLP (for Ph.D. students), taught spring 2016
At CMU, I taught courses on NLP at the undergraduate and graduate levels,
including an course originally called "Language and
Statistics II" and later "Structured Prediction for Language and Other
Discrete Data." Once I taught the graduate course "Probabilistic
Graphical Models." I regularly led advanced seminars and lab courses
on NLP. In 2013, students in the lab developed open-source morphology tools
(Assyrian) and Babylonian,
(the author of each is credited at the Github or Bitbucket site).
At various times, I co-taught with
Cohen, Chris Dyer,
Bob Frederking, and