Adaptivity in Question Answering
with User Modelling and a Dialogue Interface
Silvia Quarteroni and Suresh Manandhar
Department of Computer Science
University of York
York YO10 5DD
UK
{silvia,suresh}@cs.york.ac.uk
Abstract
Most question answering (QA) and infor-
mation retrieval (IR) systems are insensi-
tive to different users’ needs and prefer-
ences, and also to the existence of multi-
ple, complex or controversial answers. We
introduce adaptivity in QA and IR by cre-
ating a hybrid system based on a dialogue
interface and a user model. Keywords:
question answering, information retrieval,
user modelling, dialogue interfaces.
1 Introduction
While standard information retrieval (IR) systems
present the results of a query in the form of a
ranked list of relevant documents, question an-
swering (QA) systems attempt to return them in
the form of sentences (or paragraphs, or phrases),
responding more precisely to the user’s request.
However, in most state-of-the-art QA systems
the output remains independent of the questioner’s
characteristics, goals and needs. In other words,
thereisalackofusermodelling: a10-year-oldand
a University History student would get the same
answer to the question: “When did the Middle
Ages begin?”. Secondly, most of the effort of cur-
rentQAisonfactoid questions, i.e. questionscon-
cerning people, dates, etc., which can generally be
answered by a short sentence or phrase (Kwok et
al., 2001). The main QA evaluation campaign,
TREC-QA 1, has long focused on this type of
questions, for which the simplifying assumption is
that there exists only one correct answer. Even re-
centTRECcampaigns(Voorhees,2003; Voorhees,
2004) do not move sufficiently beyond the factoid
approach. They account for two types of non-
factoid questions –list and definitional– but not for
non-factoid answers. In fact, a) TREC defines list
questions as questions requiring multiple factoid
1http://trec.nist.gov
answers, b) it is clear that a definition question
may be answered by spotting definitional passages
(what is not clear is how to spot them). However,
accounting for the fact that some simple questions
may have complex or controversial answers (e.g.
“What were the causes of World War II?”) remains
an unsolved problem. We argue that in such situa-
tions returning a short paragraph or text snippet is
more appropriate than exact answer spotting. Fi-
nally, QA systems rarely interact with the user:
the typical session involves the user submitting a
queryandthesystemreturningaresult; thesession
is then concluded.
To respond to these deficiencies of existing QA
systems, we propose an adaptive system where a
QA module interacts with a user model and a di-
alogue interface (see Figure 1). The dialogue in-
terface provides the query terms to the QA mod-
ule, and the user model (UM) provides criteria
to adapt query results to the user’s needs. Given
such information, the goal of the QA module is to
be able to discriminate between simple/factoid an-
swersandmorecomplexanswers,presentingthem
in a TREC-style manner in the first case and more
appropriately in the second.
DIALOGUE
INTERFACE
QUESTION
PROCESSING
DOCUMENT
RETRIEVAL
ANSWER
EXTRACTION
USER
MODEL
Question
Answer
QA MODULE
Figure 1: High level system architecture
Related work To our knowledge, our system is
among the first to address the need for a different
approach to non-factoid (complex/controversial)
199
answers. Although the three-tiered structure of
our QA module reflects that of a typical web-
based QA system, e.g. MULDER (Kwok et al.,
2001), a significant aspect of novelty in our archi-
tecture is that the QA component is supported by
the user model. Additionally, we drastically re-
duce the amount of linguistic processing applied
duringquestionprocessingandanswergeneration,
while giving more relief to the post-retrieval phase
and to the role of the UM.
2 User model
Depending on the application of interest, the UM
can be designed to suit the information needs of
the QA module in different ways. As our current
application, YourQA2,isalearning-oriented, web-
based system, our UM consists of the user’s:
1) age range, a ∈{7 − 11,11 − 16,adult};
2) reading level, r ∈{poor,medium,good};
3) webpages of interest/bookmarks, w.
Analogies can be found with the SeAn (Ardissono
et al., 2001) and SiteIF (Magnini and Strapparava,
2001) news recommender systems where age and
browsing history, respectively, are part of the UM.
In this paper we focus on how to filter and adapt
search results using the reading level parameter.
3 Dialogue interface
The dialogue component will interact with both
the UM and the QA module. From a UM point of
view, the dialogue history will store previous con-
versations useful to construct and update a model
of the user’s interests, goals and level of under-
standing. From a QA point of view, the main goal
of the dialogue component is to provide users with
a friendly interface to build their requests. A typi-
cal scenario would start this way:
— System: Hi, how can I help you?
— User: I would like to know what books Roald Dahl wrote.
The query sentence “what books Roald Dahl wrote”, is
thus extracted and handed to the QA module. In a
second phase, the dialogue module is responsible
for providing the answer to the user once the QA
module has generated it. The dialogue manager
consults the UM to decide on the most suitable
formulation of the answer (e.g. short sentences)
and produce the final answer accordingly, e.g.:
—System: RoaldDahlwrotemanybooksforkidsandadults,
including: “The Witches”, “Charlie and the Chocolate Fac-
tory”, and “James and the Giant Peach".
2http://www.cs.york.ac.uk/aig/aqua
4 Question Answering Module
The flow between the three QA phases – question
processing, document retrieval and answer gener-
ation – is described below (see Fig. 2).
4.1 Question processing
We perform query expansion, which consists in
creating additional queries using question word
synonyms in the purpose of increasing the recall
of the search engine. Synonyms are obtained via
the WordNet 2.0 3 lexical database.
Question QUERYEXPANSION
DOCUMENT
RETRIEVAL
KEYPHRASE
EXTRACTION
ESTIMATION
OF READING
LEVELS
CLUSTERING
Language
Models
UM-BASED
FILTERING
SEMANTIC
SIMILARITY
RANKING
User Model
Reading
Level
Ranked
Answer
Candidates
Figure 2: Diagram of the QA module
4.2 Retrieval
Document retrieval We retrieve the top 20 doc-
uments returned by Google4 for each query pro-
duced via query expansion. These are processed
in the following steps, which progressively narrow
the part of the text containing relevant informa-
tion.
Keyphrase extraction Once the documents are
retrieved, we perform keyphrase extraction to de-
termine their three most relevant topics using Kea
(Witten et al., 1999), an extractor based on Naïve
Bayes classification.
Estimation of reading levels To adapt the read-
ability of the results to the user, we estimate
the reading difficulty of the retrieved documents
using the Smoothed Unigram Model (Collins-
Thompson and Callan, 2004), which proceeds in
3http://wordnet.princeton.edu
4http://www.google.com
200
two phases. 1) In the training phase, sets of repre-
sentativedocumentsarecollectedforagivennum-
ber of reading levels. Then, a unigram language
model is created for each set, i.e. a list of (word
stem, probability) entries for the words appearing
in its documents. Our models account for the fol-
lowing reading levels: poor (suitable for ages 7–
11), medium (ages 11–16) and good (adults). 2)
In the test phase, given an unclassified document
D, its estimated reading level is the model lmi
maximizing the likelihood that D ∈ lmi5.
Clustering We use the extracted topics and es-
timated reading levels as features to apply hierar-
chical clustering on the documents. We use the
WEKA (Witten and Frank, 2000) implementation
of the Cobweb algorithm. This produces a tree
where each leaf corresponds to one document, and
sibling leaves denote documents with similar top-
ics and reading difficulty.
4.3 Answer extraction
In this phase, the clustered documents are filtered
based on the user model and answer sentences are
located and formatted for presentation.
UM-based filtering The documents in the clus-
ter tree are filtered according to their reading diffi-
culty: only those compatible with the UM’s read-
ing level are retained for further analysis6.
Semantic similarity Within each of the retained
documents, we seek the sentences which are se-
mantically most relevant to the query by applying
the metric in (Alfonseca et al., 2001): we rep-
resent each document sentence p and the query
q as word sets P = {pw1,...,pwm} and Q =
{qw1,...,qwn}. The distance from p to q is then
distq(p) = summationtext1≤i≤m minj[d(pwi,qwj)], where
d(pwi,qwj) is the word-level distance between
pwi and qwj based on (Jiang and Conrath, 1997).
Ranking Given the query q, we thus locate
in each document D the sentence p∗ such that
p∗ = argminp∈D[distq(p)]; then, distq(p∗) be-
comes the document score. Moreover, each clus-
5The likelihood is estimated using the formula:
Li,D = summationtextw∈D C(w,D) · log(P(w|lmi)), where w is a
word in the document, C(w,d) is the number of occurrences
of w in D and P(w|lmi) is the probability with which w
occurs in lmi
6However, if their number does not exceed a given thresh-
old, we accept in our candidate set part of the documents hav-
ingthenextlowestreadability–oramediumreadabilityifthe
user’s reading level is low
ter is assigned a score consisting in the maximal
score of the documents composing it. This allows
to rank not only documents, but also clusters, and
presentresultsgroupedbyclusterindecreasingor-
der of document score.
Answer presentation We present our answers
in an HTML page, where results are listed follow-
ing the ranking described above. Each result con-
sists of the title and clickable URL of the originat-
ing document, and the passage where the sentence
which best answers the query is located and high-
lighted. Question keywords and potentially useful
information such as named entities are in colour.
5 Sample result
We have been running our system on a range
of queries, including factoid/simple, complex and
controversial ones. As an example of the latter, we
report the query “Who wrote the Iliad?”, which is
a subject of debate. These are some top results:
— UMgood: “Most Classicists would agree that, whether
there was ever such a composer as "Homer" or not, the
Homeric poems are the product of an oral tradition [...]
Could the Iliad and Odyssey have been oral-formulaic po-
ems, composed on the spot by the poet using a collection of
memorized traditional verses and phases?”
— UMmed: “No reliable ancient evidence for Homer –
[...] General ancient assumption that same poet wrote Il-
iad and Odyssey (and possibly other poems) questioned by
many modern scholars: differences explained biographi-
cally in ancient world (e g wrote Od. in old age); but simi-
larities could be due to imitation.”
— UMpoor: “Homer wrote The Iliad and The Odyssey
(at least, supposedly a blind bard named "Homer" did).”
In the three results, the problem of attribution of
the Iliad is made clearly visible: document pas-
sages provide a context which helps to explain the
controversy at different levels of difficulty.
6 Evaluation
Since YourQA does not single out one correct an-
swerphrase,TRECevaluationmetricsarenotsuit-
able for it. A user-centred methodology to assess
how individual information needs are met is more
appropriate. Webaseourevaluationon(Su,2003),
which proposes a comprehensive search engine
evaluation model, defining the following metrics:
1. Relevance: we define strict precision (P1) as
the ratio between the number of results rated as
relevant and all the returned results, and loose pre-
201
cision (P2) as the ratio between the number of re-
sults rated as relevant or partially relevant and all
the returned results.
2. Usersatisfaction: a7-pointLikertscale7 isused
to assess the user’s satisfaction with loose preci-
sion of results (S1) and query success (S2).
3. Reading level accuracy: given the set R of re-
sults returned for a reading level r, Ar is the ratio
between the number of results ∈ R rated by the
users as suitable for r and |R|.
4. Overall utility (U): the search session as a
whole is assessed via a 7-point Likert scale.
We performed our evaluation by running 24
queries (some of which in Tab. 2) on Google and
YourQA and submitting the results –i.e. Google
result page snippets and YourQA passages– of
both to 20 evaluators, along with a questionnaire.
The relevance results (P1 and P2) in Tab. 1 show a
P1 P2 S1 S2 U
Google 0,39 0,63 4,70 4,61 4,59
YourQA 0,51 0,79 5,39 5,39 5,57
Table 1: Evaluation results
10-15% difference in favour of YourQA for both
strict and loose precision. The coarse seman-
tic processing applied and context visualisation
thus contribute to creating more relevant passages.
Both user satisfaction results (S1 and S2) in Tab.
1 also denote a higher level of satisfaction tributed
toYourQA.Tab. 2showsthatevaluatorsfoundour
Query Ag Am Ap
When did the Middle Ages begin? 0,91 0,82 0,68
Who painted the Sistine Chapel? 0,85 0,72 0,79
When did the Romans invade Britain? 0,87 0,74 0,82
Who was a famous cubist? 0,90 0,75 0,85
Who was the first American in space? 0,94 0,80 0,72
Definition of metaphor 0,95 0,81 0,38
average 0,94 0,85 0,72
Table 2: Sample queries and accuracy values
results appropriate for the reading levels to which
they were assigned. The accuracy tended to de-
crease (from 94% to 72%) with the level: it is
indeed more constraining to conform to a lower
reading level than to a higher one. Finally, the
7This measure – ranging from 1= “extremely unsatisfac-
tory” to 7=“extremely satisfactory” – is particularly suitable
to assess how well a system meets user’s search needs.
general satisfaction values for U in Tab. 1 show
an improved preference for YourQA.
7 Conclusion
A user-tailored QA system is proposed where a
user model contributes to adapting answers to the
user’s needs and presenting them appropriately.
A preliminary evaluation of our core QA module
shows a positive feedback from human assessors.
Our short term goals involve performing a more
extensive evaluation and implementing a dialogue
interface to improve the system’s interactivity.
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