[Colloquium] Re: REMINDER: 10/21 Distinguished Lecture Series: Christopher Manning, Stanford

[Mary Marre via Colloquium]

*Distinguished Lecture Series:  *

*Christopher Manning, Stanford*

 ​
​​
*Friday, October 21, 2016 at 11:00 am*

*Toyota Technological Institute at Chicago*

*6045 S. Kenwood Avenue*

*Room #526**​**/530*

*Christopher Manning, PhD*

Professor of Linguistics and Computer Science
Natural Language Processing Group
Stanford University



*homepage* <http://nlp.stanford.edu/manning>



*Title*: Reading Comprehension and Natural Language Inference



*Abstract*: Much of computational linguistics and text understanding is
either towards one end of the spectrum where there is no representation of
compositional linguistic structure (bag-of-words models) or near the other
extreme where very complex representations are employed (first order logic,
AMR, HPSG, ...). A unifying theme of much of my recent work is to explore
models with just a little bit of appropriate linguistic structure. I will
focus here on recent case studies in reading comprehension and question
answering, exploring the use of both natural logic and deep learning
methods for reading comprehension and question answering.

Enabling a computer to understand a document so that it can answer
comprehension questions is a central, yet still unsolved goal of NLP. I’ll
first introduce our recent work on the Deepmind QA dataset - a recently
released dataset of millions of examples constructed from news articles. On
the one hand, we show that (simple) neural network models are surprisingly
good at solving this task and achieving state-of-the-art accuracies; on the
other hand, we did a careful hand-analysis of a small subset of the
problems, and we argue that we are quite close to a performance ceiling on
this dataset, and it is still quite far from genuine deep / complex
understanding .I will then turn to the use of Natural Logic, a weak proof
theory on surface linguistic forms which can nevertheless model many of the
common-sense inferences that we wish to make over human language material.
I will show how it can support common-sense reasoning and be part of a more
linguistically based approach to open information extraction which
outperforms previous systems. I show how to augment this approach with a
shallow lexical classifier to handle situations where we cannot find any
supporting premises. With this augmentation, the system gets very promising
results on answering 4th grade science questions, improving over both the
classifier in isolation, a strong IR baseline, and prior work. Finally, I
will look at how we can incorporate more of the compositional structure of
language, which is standardly used in logical approaches to understanding,
into a deep learning model. I will emphasize some reason work which shows
how that can be done quite efficiently by building the structure like a
shift-reduce parser, and how the resulting system can produce stronger
results than a sequence model on a natural language inference task.



The talk will include joint work with Gabor Angeli, Danqi Chen, and Sam
Bowman.



*Bio*: Christopher Manning is a professor of computer science and
linguistics at Stanford University. His Ph.D. is from Stanford in 1995, and
he held faculty positions at Carnegie Mellon University and the University
of Sydney before returning to Stanford. His research goal is computers that
can intelligently process, understand, and generate human language
material. Manning concentrates on machine learning approaches to
computational linguistic problems, including syntactic parsing,
computational semantics and pragmatics, textual inference, machine
translation, and using deep learning for NLP. He is an ACM Fellow, a AAAI
Fellow, and an ACL Fellow, and has coauthored leading textbooks on
statistical natural language processing and information retrieval. He is a
member of the Stanford NLP group (@stanfordnlp).



Host: Kevin Gimpel, kgimpel at ttic.edu



Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*
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