From TREC to Watson: is open domain question answering a solved problem?

ConstantinOrasan
Research Group in Computational Linguistics,
University of Wolverhampton, UK
http://www.wlv.ac.uk/~in6093/
From TREC to Watson: is open domain question answering a solved problem?

Structure of the talk
4 July 2011
Constantin Orasan - KEPT 2011
Brief introduction to QA
Video 1: Where are we now – IBM Watson
The structure of a QA system
Video 2: Watson vs. humans
Overview of Watson
QA from the point of view of users/companies
Conclusions

Information overload
4 July 2011
“Getting information off the Internet is like taking a drink from a fire hydrant”
Mitchell Kapor

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 languagein 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

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

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.

Where are we now?
4 July 2011
IBM and the Jeopardy Challenge
Jeopardy! 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 IBM
http://www.youtube.com/watch?v=FC3IryWr4c8

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

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

Expected answer type calculation
4 July 2011
Relies on the existence of an answer type taxonomy
This taxonomy can be made open-domain by linking to general ontologies such as WordNet
The 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

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

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)

Answer extraction
4 July 2011
Uses a variety of techniques to identify the answer of a question
The answer should have the type of EAT
Very 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

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

Open domain QA - evaluation
4 July 2011
Great coverage, but low accuracy
For 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?

How did Watson perform?
4 July 2011
http://www.youtube.com/watch?v=Puhs2LuO3Zc

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

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 heavily
Lots of components were developed to deal with specific problems (lots of experts)
Watson tries to combine deep and shallow knowledge
Had access to large data sets and very good hardware

Overview of Watson’s structure
4 July 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)

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 architecture
Indexes used to answer the questions had to be pre-processed using Hadoop

Watson was not only NLP
4 July 2011
Betting strategyhttp://www.youtube.com/watch?v=vA9aqAd2iso

To sum up, Watson is:
4 July 2011
An amazing engineering project
A massive investment
Research in many domains of NLP
A big PR stunt
A 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

QALL-ME framework
4 July 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

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

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

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.

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 questions
Provide a more natural way of locating a needed information
Companies 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].
26/05/2011
Knowledge acquisition from Wikipedia for IQA
35

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

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

Watson is not always right
4 July 2011
but it kind of knows this ….
http://www.youtube.com/watch?v=7h4baBEi0iA

Thank you for your attention
4 July 2011

From TREC to Watson: is open domain question answering a solved problem?

by Constantin Orasan

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