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CONSTANTIN ORASAN
RESEARCH GROUP IN COMPUTATIONAL LINGUISTICS,
UNIVERSITY OF WOLVERHAMPTON, UKHTTP: / /WWW.WLV.AC.UK/~IN6093/
From TREC to Watson: is open domain question answering a
solved problem?
Constantin Orasan - KEPT 2011
Structure of the talk
4 July 2011
1. Brief introduction to QA
Video 1: Where are we now – IBM Watson
2. The structure of a QA system
Video 2: Watson vs. humans
3. Overview of Watson
4. QA from the point of view of users/companies
5. Conclusions
Constantin Orasan - KEPT 2011
Information overload
4 July 2011
“Getting information off the Internet is like taking a drink from a fire hydrant”
Mitchell Kapor
Constantin Orasan - KEPT 2011
What is question answering?
4 July 2011
A way to address the problem of information overload
Question answering aims at identifying the answer to a question posed in natural language in a large collection of documents
The information provided by QA is more focused than information retrieval
The output can be the exact answer or a text snippet which contains the answer
The domain took off as a result of the introduction of QA track in TREC, whilst cross-lingual QA as a result of CLEF
Constantin Orasan - KEPT 2011
Types of QA systems
4 July 2011
open-domain QA systems: can answer any question from any collection+ can potentially answer any question- very low accuracy (especially in cross-lingual settings)
canned QA systems: rely on a very large repository of questions for which the answer is known+ very little language processing necessary- limited to the answers in the database
closed-domain QA systems: are built for very specific domains and exploit expert knowledge in them+ very high accuracy- can require extensive language processing and limited to one domain
Constantin Orasan - KEPT 2011
Evolution of QA domain
4 July 2011
Early QA systems date as back as 1960s and were mainly front ends to
databases had limited usability
Open-domain QA emerged as a result of the increasing amount of data available to answer a question need to find and extract the answer developed last 1990s as a result of the QA track at Text
REtrieval Conferences emphasis on factoid questions, but other types of questions
were also explored CLEF competitions have encouraged development of cross-
lingual systems.
Constantin Orasan - KEPT 2011
Where are we now?
4 July 2011
IBM and the Jeopardy ChallengeJeopardy! is an American quiz show where
participants are given clues and need to guess the question (e.g. if the clue is The Father of Our Country; he didn't really chop down a cherry tree the contestant would respond Who is George Washington?)
Watson is a QA system developed by IBMhttp://
www.youtube.com/watch?v=FC3IryWr4c8
Constantin Orasan - KEPT 2011
Structure of an open domain QA system
4 July 2011
A typical open domain QA system consists of: Question processor Document processor Answer extractor (and validation)
Can have components for cross-lingual processing
Has access to several external resources
Constantin Orasan - KEPT 2011
Question processor
4 July 2011
Produces an interpretation of the question Determines the Question Type (e.g. factoid, definition,
procedure, etc.) Determines the Expected Answer Type (EAT) On the basis of the question it produces a query Determines syntactic and semantic relations between
the words from the questions Expands the query with synonyms May perform translation of the keywords in the query
in the case of cross-lingual QA
Constantin Orasan - KEPT 2011
Expected answer type calculation
4 July 2011
Relies on the existence of an answer type taxonomyThis taxonomy can be made open-domain by linking
to general ontologies such as WordNetThe EAT can be determined using rule-based as
well as machine learning approaches
Who is the president of Romania?Where is Paris?
Knowledge of domain can greatly improve the identification of EAT and help deal with ambiguities
Constantin Orasan - KEPT 2011
Query formulation
4 July 2011
Produces a query from the question As a list of keywords As a list of phrases Identifies entities present in the question
Produce variants of the query by introducing morphological, lexical and semantic variations
Domain knowledge is very important for identification of entities and
generation of valid variations and vital in cross-lingual scenarios
Constantin Orasan - KEPT 2011
Document processing
4 July 2011
Uses the query produced in the previous step to retrieve paragraphs which may contain the answer
It is largely domain independent as it relies on text retrieval engines
Ranks results, but this is largely independent of the QA task
For limited collections of texts it is possible to enrich the index with various linguistic information which can help further processing
When the domain is known, characteristics of the input files can improve the retrieval (e.g. presence of metadata)
Constantin Orasan - KEPT 2011
Answer extraction
4 July 2011
Uses a variety of techniques to identify the answer of a question
The answer should have the type of EATVery often rely on previously created patterns (e.g.
When was the telephone invented? can be answered if there is a sentence that matches the pattern The telephone was invented in <date>),
Many patterns can express the same answer (e.g. the telephone, invented in <date>)
Relations identified in the question between the expected answer and entities from the question can be exploited by patterns
Constantin Orasan - KEPT 2011
Answer extraction (II)
4 July 2011
Potential answers are ranked according to functions which are usually learned from the data
The ranking and validation of answers can be done using external sources such as the Internet
QA for well defined domains can rely on better patterns
The functions learned usually work well only on the type of data used for training
Constantin Orasan - KEPT 2011
Open domain QA - evaluation
4 July 2011
Great coverage, but low accuracyFor example:
EPHYRA QA system in TRAC 2007 reports an accuracy of 0.20 for factoid questions (Schlaefer et al. 2007)
OpenEphyra was used for a cross-lingual Romanian – English QA system and we obtained 0.11 accuracy for factoid questions (Dornescu et al. 2008) – the best performing system for all cross-lingual QA tasks in CLEF 2008
The results are not directly comparable (different QA engines, tuned differently, different collections, different tasks)
But does it make sense to do open domain question answering?
Constantin Orasan - KEPT 2011
How did Watson perform?
4 July 2011
http://www.youtube.com/watch?v=Puhs2LuO3Zc
Constantin Orasan - KEPT 2011
How was this achieved?
4 July 2011
Starting point the Practical Intelligent Question Answering Technology (PIQUANT) developed by IBM to participate in TREC
Has been under development at IBM for more than 6 years by a team of 4 full time researchers
Was one of the top three to five in many TRECs
PIQUANT was performing around 0.33 on the TREC data
PIQUANT used a standard architecture for QA
Constantin Orasan - KEPT 2011
How was this achieved? (II)
4 July 2011
Lots of extra work was put in the system: a core team of 20 researchers working for almost 4 years
PIQUANT system was enriched with a large number of modules for language processing
The processing was parallelised heavilyLots of components were developed to deal with
specific problems (lots of experts)Watson tries to combine deep and shallow
knowledgeHad access to large data sets and very good
hardware
Constantin Orasan - KEPT 2011
Overview of Watson’s structure
4 July 2011
Constantin Orasan - KEPT 2011
Hardware used
4 July 2011
Watson is a workload optimized system designed for complex analytics, made possible by integrating massively parallel POWER7 processors and the IBM DeepQA software to answer Jeopardy! questions in under three seconds. Watson is made up of a cluster of ninety IBM Power 750 servers (plus additional I/O, network and cluster controller nodes in 10 racks) with a total of 2880 POWER7 processor cores and 16 Terabytes of RAM. Each Power 750 server uses a 3.5 GHz POWER7 eight core processor, with four threads per core. The POWER7 processor's massively parallel processing capability is an ideal match for Watson's IBM DeepQA software which is embarrassingly parallel (that is a workload that is easily split up into multiple parallel tasks).
According to John Rennie, Watson can process 500 gigabytes, the equivalent of a million books, per second. IBM's master inventor and senior consultant Tony Pearson estimated Watson's hardware cost at about $3 million and with 80 TeraFLOPs would be placed 94th on the Top 500 Supercomputers list.
From: http://en.wikipedia.org/wiki/Watson_(computer)
Constantin Orasan - KEPT 2011
Speed of answer
4 July 2011
In Jeopardy! an answer needs to be provided in 3-5 seconds
In initial experiments with running Watson on a single processor an answer was obtained in about 2 hours
The system was implemented using Apache UIMA Asynchronous Scaleout
Massively parallel architectureIndexes used to answer the questions had to
be pre-processed using Hadoop
Constantin Orasan - KEPT 2011
Watson was not only NLP
4 July 2011
Betting strategyhttp://www.youtube.com/watch?v=vA9aqAd2iso
Constantin Orasan - KEPT 2011
To sum up, Watson is:
4 July 2011
An amazing engineering projectA massive investmentResearch in many domains of NLPA big PR stuntA way to improve the IBM position in text
analytics
But it is not really a technology ready to be deployed
But was it a real progress in open-domain QA?
So is open domain QA a solved problem?
Can we really solve open domain QA?
Do we really need open domain QA?
Do we care?
QA from user perspective
Real user questions Are rarely open domain Can rarely be formulated in one go Do not always contain answers from only one
source
Companies Have very well defined needs Have access to previously asked questions Need very high accuracy Most of them cannot afford to invest millions of
dollars
The QALL-ME project
Question Answering Learning technologies in a multiLingual and Multimodal Environment (QALL-ME) – FP6 funded project on Multilingual and Multimodal Question Answering FBK, Trento, Italy – coordinator University of Wolverhampton, UK DFKI, Germany University of Alicante, Spain Comdata, Italy Ubiest, Italy Waycom, Italy
http://qallme.fbk.eu Has established an infrastructure for multilingual
and multimodal question answering
The QALL-ME project
demonstrators in domain of tourism – can answer questions in the domain of cinema/movies and accommodation.
E.g. What movies can I see in Wolverhampton this
week? How can I get to Novotel Hotel, Wolverhampton?
the questions can be asked in any of the four languages in the consortium
small scale demonstrator built for Romanian
Constantin Orasan - KEPT 2011
QALL-ME framework
4 July 2011
Constantin Orasan - KEPT 2011
The QALL-ME ontology
4 July 2011
All the reasoning and processing is done using a domain ontology
The ontology also provides the means of achieving cross-lingual QA
Determines the way data is stored in the database
Ontologies need to be developed for each domain
30
Part of the tourism ontology
MovieShow
Cinema
Movie
TicketPrice
DateTimePeriod
synposis
isInSitehasPrice
hasEventContent
hasPeriod
priceType
priceValue
Director
Star
Producer
Writer
Currency
GPSCoordinate
DirectionLocation
Contact
hasCurrency
TimePeriod
DatePeriod
startTimeendTime
endDate startDate
hasTimePeriod
hasDatePeriod
DirectionLocation
hasSiteFacility
hasContact
hasWriter
hasDirector
hasProducer
genre
name
hasPostalAddress
hasGPSCoordinate
PostalAddress
CinemaRoom
hasRoom
hasStar
certificate
SitePrice
Event
EventContentPeriod
subClassOfsubClassOf
subClassOf
subClassOfsubClassOf
SiteFacility
RoomFacility
hasRoomFacility
name description
Constantin Orasan - KEPT 2011
Evaluation of the QALL-ME prototype
4 July 2011
For the cinema domain the accuracy ranged between 60% to 85% depending on the language
The system was tested on real questions posed by the users which were completely independent from the ones used to develop the system
The error were mainly caused by wrongly identified named entities, missing patterns and mistakes of the entailment engine
In an commercial environment this system can be revised every day in order to obtain much higher performance
Constantin Orasan - KEPT 2011
Closed domain QA for commercial companies
4 July 2011
Closed domain QA has a certain appeal with companies
These companies normally have large databases of questions and answers from customers
The domain can be very clearly defined
In some cases the systems needed are actually canned QA systems
Constantin Orasan - KEPT 2011
Interactive QA
4 July 2011
It is easy to ask:
Where can I eat paella tonight?
but what about:
What mobile phones are smart phones with a camera, have GPS, have touch screen, are produced by HTC or Nokia, but definitely not Samsung, have 1G internal memory ….
Interactivity is needed to deal with ambiguities, wrong answers, user feedback, etc.
Constantin Orasan - KEPT 2011
Interactive QA
4 July 2011
“process where the user is a continual part of the information loop”
At intersection of Dialogue Systems and Question Answering
Initiate dialogue with the user in cases where there are too many or too few answers, or there is some ambiguity in the request
Can suggest follow up questionsProvide a more natural way of locating a needed
informationCompanies are interested in developing IQA systems
as a way for providing customer services
Hypothetical IQA session
[1] SYS: Hi![2] USER: Can you find me a smart phone with a camera?[3] SYS: There are [NUMBER][big number] of smart phones featuring a
camera, would you like to search for some [BRAND]?[4] USER: No, which have GPS?[5] SYS: [STILL BIG NUMBER] have GPS, are you interested in ones
having TOUCH SCREEN?[Suggesting new constraints][6] USER: Yes, it would be great.[7] SYS: [NUMBER] of Nokia phones, [NUMBER] HTC phones,
[NUMBER] Samsung phones, [NUMBER] of other brands.[8] USER: Ok, what about HTC phones?[9] SYS: [NUMBER] of them have [feature], [NUMBER] of them have
[feature].[10] USER: What are the ones with [feature]?[11] SYS: Here you are: [RESULTS].
26/05/2011Knowledge acquisition from Wikipedia for IQA
35
Constantin Orasan - KEPT 2011
Answers from more than one source
4 July 2011
Many complex questions need to compose the answer to a question from several sources: List questions:
List all the cantons in Switzerland which border Germany
Sentiment questions:What features people like in Vista?
This is part of the new trend in “deep QA”Even though users probably really need such
answers, the technology is still at the stage of research projects
Constantin Orasan - KEPT 2011
To sum up …
4 July 2011
Some researchers believe that search is dead and “deep QA” is the future
This was largely fuelled by IBM’s Watson’s winning the Jeopardy!
Watson is a fantastic QA system, but it does not solve the problem of open domain QA
For real applications we still want to focus on very well defined domains
We still want to have the user in the loop to facilitate asking questions
Watson may have revived the interest in QA
Constantin Orasan - KEPT 2011
Watson is not always right
4 July 2011
but it kind of knows this ….
http://www.youtube.com/watch?v=7h4baBEi0iA
Constantin Orasan - KEPT 2011
Thank you for your attention
4 July 2011