My research focuses on the development of deep learning techniques for natural language tasks such as translation and summarization. Currently I'm focused on improving the interpretability of deep learning — in particular, devising internal representations of text that can be better understood by humans, while retaining the expressive power and flexibility of deep learning.
I have a blog, where I write about mine and others' research.
- February 2018 — I moderated a debate between Yann LeCun and Chris Manning on deep learning, structure and innate priors.
- January 2018 — I'm a head TA for CS224n: Natural Language Processing with Deep Learning, and will be giving some lectures for the class.
- August 2017 — I attended ACL 2017 in Vancouver. Read my thoughts on the conference here.
- July 2017 — At SAILORS 2017, I instructed eight high-schoolers to build a Naive Bayes classifier to classify tweets in a disaster relief setting (materials here).
- May 2017 — I received an NVIDIA Graduate Fellowship. Thank you NVIDIA!
- April 2017 — Our paper on summarization has been accepted to ACL — check out the blog post! I started this project during my Google Brain internship, then continued it at Stanford.
- November 2016 — I spoke to Melinda Gates about the importance of women in AI, both on a personal level, and to society at large.
- August 2016 — Attended ACL and presented my poster at CoNLL.
July 2016 — I gave two tutorials at SAILORS, Stanford AI's outreach program for high school girls. One tutorial was on graph search algorithms applied to path-finding, and the other on the nearest neighbor algorithm applied to movie recommendations (materials here). The students really impressed me with their talent and enthusiasm!
- June 2016 — Started summer internship at Google Brain, hosted by Peter J Liu. I'll be working on automatic text summarization.
- June 2016 — Our paper has been accepted to CoNLL. See you in Berlin!
- April 2016 — I was a mentor for the AI track of Girls Teaching Girls To Code.
Get To The Point: Summarization with Pointer-Generator Networks
Abigail See, Peter J. Liu, Christopher D. Manning
Association for Computational Linguistics (ACL). 2017.
[blog post | poster (PDF | Keynote) | slides]
Compression of Neural Machine Translation Models via Pruning
Abigail See, Minh-Thang Luong, Christopher D. Manning
Computational Natural Language Learning (CoNLL). 2016.
[poster | spotlight slides]
The Cost of Principles: Analyzing Power in Compatibility Weighted Voting Games
Abigail See, Yoram Bachrach, Pushmeet Kohli
Autonomous Agents and Multi-Agent Systems (AAMAS). 2014.
- For the 2017-2018 academic year, I'm the organizer of AI Salon, a discussion series in the AI Lab.
- Teaching resources for the SAILORS 2017 NLP research project: Tweet Classification for Disaster Relief.
- Slides for my tutorial on Graph Search for SAILORS.
- Slides and code for my Nearest Neighbors movie recommender system tutorial for SAILORS and GTGTC.
- Smoothed Analysis with Applications in Machine Learning, an essay I wrote for the Masters in Mathematics course at Cambridge University.
I'm originally from Cambridge in the UK, though I've also lived in Singapore. In 2014 I graduated with a MMath from Cambridge University's Mathematical Tripos (to read about the many peculiarities of the Tripos, see here). While at Cambridge my interests were Pure Mathematics — particularly Combinatorics, Logic and Operational Research.
During my undergraduate degree I became interested in Computer Science by interning twice at Microsoft Research Cambridge. In 2012 I worked with the Programming Principles and Tools group on the T2 project, and in 2013 I worked on co-operative Game Theory.
In my spare time I enjoy social dance, watching and discussing films, and writing.
Here is my CV.