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Isabelle Augenstein, UCL / UCPH
Machine Reading Using
Neural Machines
Goal: Fact Checking
query
“Unemployment in the US is 42%”
Machine	
Reader	
unemployment(US, 42%)
What	is	the	stance	of	
HRC	on	immigra3on?	
stance(HRC,	
immigra3on,	X)	
SemEval 2016 Stance Detection, EMNLP 2016,
SemEval 2017 RumourEval, Fake News Challenge 2017
Goal: Understanding Scientific Publications
query
Machine	
Reader	
Method	A	outperforms	
method	B	for	task	C	
	
outperforms(A,	B,	C)	
What	models	exists	for	ques3on	
answering?	
	
method_for_task(QA)	
SemEval 2017 ScienceIE (organiser), ACL 2017, ConLL 2017
What	models	exists	for	
ques3on	answering?	
	
method_for_task(QA)	
Machine	
Reader	
query	
query	
Ques%ons	
	
	
	
	
	
	
	
	
	
What	is	the	stance	of	
HRC	on	immigra3on?	
stance(HRC,	
immigra3on,	X)
What	models	exists	for	
ques3on	answering?	
	
method_for_task(QA)	
Machine	
Reader	
query	
query	
retrieval	
RNN,	method-for,	QA	
“We	
introduce	a	
RNN-based	
method	for	
QA”	
“Immigrants	welcome!”	
	
	
	
	
	
	
	
Evidence	
Ques%ons	
	
	
	
	
	
	
	
	
	
What	is	the	stance	of	
HRC	on	immigra3on?	
stance(HRC,	
immigra3on,	X)
What	models	exists	for	
ques3on	answering?	
	
method_for_task(QA)	
Machine	
Reader	
query	
query	
retrieval	
RNN,	method-for,	QA	
“We	
introduce	a	
RNN-based	
method	for	
QA”	
“Immigrants	welcome!”	
method_for_task(QA)	
	
	
	
	
	
Representa%ons	
updates	
answers	
	
	
	
	
	
	
	
Evidence	
Ques%ons	
	
	
	
	
	
	
	
	
	
What	is	the	stance	of	
HRC	on	immigra3on?	
stance(HRC,	
immigra3on,	X)
What	models	exists	for	
ques3on	answering?	
	
method_for_task(QA)	
Machine	
Reader	
query	
query	
Methods	based	on	
RNNs	are	widely	used	
	
RNNs	
answers	
HRC	is	in	favour	of	
immigra3on	
	
favour	
answers	
retrieval	
RNN,	method-for,	QA	
“We	
introduce	a	
RNN-based	
method	for	
QA”	
“Immigrants	welcome!”	
method_for_task(QA)	
	
	
	
	
	
Representa%ons	
updates	
answers	
	
	
	
	
	
	
	
Evidence	
Ques%ons	
	
	
	
	
	
	
	
	
	
Answers	
	
	
	
	
	
	
	
	
	
What	is	the	stance	of	
HRC	on	immigra3on?	
stance(HRC,	
immigra3on,	X)
Task: Stance Detection
Document: “… Led Zeppelin’s Robert Plant turned down £500 MILLION to reform
supergroup. …”
Headline/Target: “Robert Plant Ripped up $800M Led Zeppelin Reunion Contract
-> Confirming
Task: How do sequences relate to one another? (e.g. for, against, neutral)
Problems:
•  Interpretation depends on headline/target
•  Target not always seen in training set
•  However, overlap between target + document
Fake News Challenge 2017
Task: Stance Detection
Tweet:
Target: Legalization of Abortion, Atheism, Pro Life, …
Task: How do sequences relate to one another? (e.g. for, against, neutral)
Problems:
•  Interpretation depends on target
•  Target not always mentioned in tweet
•  No training data for test target -> target-independent / unseen headline
approach needed
SemEval 2016 Stance Detection, EMNLP 2016
A	foetus	has	rights	too!
Task: Scientific Paper Summarisation
Select the sentences
from within a paper
which best summarise
that paper. Treated as
a binary classification
task - each sentence
classified as either
summary or not.
The Task
Challenges
•  Extractive summarisation
Ø  Binary classification task:
for each sentence, is it
summary statement or not?
•  Fine neural encoding of
current sentence
•  Simple, coarse features for
paragraph and global (e.g.
location) features
ConLL 2017
Science Paper Summarisation Dataset
Paper title Statistical estimation of the names of HTTPS servers
Author-Written Highlights (= Summary Statements!)
-  We present the domain name graph (DNG), which is a formal expression that
can keep track of cname chains and (…)
-  …
Summary statements highlighted in main text:
In this work, we present a novel methodology that aims to (…)
The key contributions of this work are as follows.
We present the domain name graph (DNG), which is a formal expression that can
keep track of cname chains (challenge 1) and (…)
ConLL 2017
Research Challenges
-  Small Datasets
-  Weak supervision, multi-task learning, semi-supervision
-  Computational cost of neural machine reading
-  Makes small benchmark datasets more attractive for research
-  Few datasets with large or multiple documents
-  Proliferation of datasets, some toy tasks or not well designed
-  E.g. small vocabulary, easy to “game”
Thank you!
Questions?
Papers at ACL 2017: ACL 2017, ConLL 2017, SemEval 2017 ScienceIE
(organiser), SemEval 2017 RumourEval
augenstein.github.io
augenstein@di.ku.dk
@iaugenstein
github.com/isabelleaugenstein

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