[Notice: what an unfortunate timing! This post is definitely NOT an april fool’s joke.] Sebastien Jean and I had a paper titled <context-aware learning for neural machine translation> rejected from NAACL’19, perhaps understandable because we did not report any substantial gain in the BLEU score. As I finally found some time to read Pearl’s <Book of Why> due to a personal reason (yes, personal reasons sometimes can help), I thought I wrote a short note on how the idea in this paper was originally motivated. As I was never educated in causal inference or learning, I was scared of using a term
Category: Research
Lecture note “Brief Introduction to Machine Learning without Deep Learning”
This past Spring (2017), I taught the undergrad <Intro to Machine Learning> course. This was not only the first time for me to teach <Intro to Machine Learning> but also the first time for me to teach an undergrad course (!) This course was taught a year before by David Sontag who has now moved to MIT. Obviously, I thought about re-using David’s materials as they were, which you can find at http://cs.nyu.edu/~dsontag/courses/ml16/. These materials are really great, and the coverage of various topics in ML is simply amazing. I highly recommend all the materials on this web page. All the things
to arXiv or not to arXiv
I believe it is a universal phenomenon: when you’re swamped with work, you suddenly feel the irresistible urge to do something else. This is one of those something else. Back in January (2016), right after the submission deadline of NAACL’16, Chris Dyer famously (?) posted on this Facebook wall, “to arxiv or not to arxiv, that is the increasingly annoying question.” This question of “to arxiv or not to arxiv” a conference submission, that has not yet gone through peer-review, indeed has become a thorny issue in the field of machine learning and a wider research community around it, including
DeepMind Q&A Data
One major issue with research in Q&A is that there is not a controlled but large-scale standard benchmark dataset available. There are a number of open-ended Q&A datasets, but they often require a system to have access to external resources. This makes it difficult for researchers to compare different models in a unified way. Recently, one such large-scale standard Q&A dataset was proposed by Hermann et al. (2015). In this dataset, a question comes along with a context of which one of the word is an answer to the question. And.. wait.. I just realized that I don’t have to
Lecture Note for “NLP with Distributed Representation” on arXiv Now
On the same day I moved to NYC at the end of August, I had coffee with Hal Daume III. Among many things we talked about, I just had to ask Hal for advice on teaching, as my very first full-semester course was about to start then. One of the first questions I asked was whether he had some lectures slides all ready now that it’s been some years since he’s started teaching. His response was that there was no slide! No slide? I was shocked for a moment. Though, now that I think about it, most of the