Simulating Cub Reporter Dialogues: The collection of naturalistic
human-human dialogues for information access to text archives
Emma Barker∗ , Ryuichiro Higashinaka∗† , François Mairesse∗ ,Robert Gaizauskas∗ , Marilyn Walker∗ , Jonathan Foster∗∗∗

Department of Computer Science/∗∗ Department of Journalism, University of Sheffield, UK†
NTT Communication Science Laboratories, NTT Corporation, Japan
Abstract

This paper describes a dialogue data collection experiment and resulting corpus for dialogues between a senior mobile journalist anda junior cub reporter back at the office. The purpose of the dialogue is for the mobile journalist to collect background information in
preparation for an interview or on-the-site coverage of a breaking story. The cub reporter has access to text archives that contain suchbackground information. A unique aspect of these dialogues is that they capture information-seeking behavior for an open-ended task
against a large unstructured data source. Initial analyses of the corpus shows that the experimental design leads to real-time, mixedinitiative, highly interactive dialogues with many interesting properties.

1. Background and Motivation
For decades now it has been widely assumed that people
seeking information from large-scale, electronic information resources want the option to communicate with theinformation access system as if they were communicatingwith another human, i.e. through a sequence of questions
and responses forming a dialogue in which either party may
take the initiative. Such information-seeking scenarios may
be characterised by the nature of the information-seeking
task – more or less focussed – and by the nature of the resource in which the information is being sought – more orless structured. Much work has been done on dialogues for
information-seeking for focussed tasks against structured
databases (Walker et al., 2002) . Recently work has also
been done on dialogues for information-seeking for lessfocussed tasks (Stede and Schlangen, 2004; Denecke andYasuda, 2005), and where the information source is an unstructured, open domain text collection (Baeza-Yates andRibiero-Neto, 1999). Only recently has this work begun toconsider question asking and answering as potential meansof access (Voorhees, 2005), and it has not yet advanced tothe point of considering unconstrained dialogues for thisinformation-seeking scenario. However the ability to communicate via a dialogue with an information agent that has
full knowledge of the content of a large text collection isclearly highly desirable (Burger et al., 2002).
The problem is that we do not know how to build systems that can do this, nor do we understand the character of naturalistic dialogues for access to open domain
text collections, especially for less focussed and complex
information-seeking tasks. As other work in human language technology has shown, a key step in advancing ourknowledge is the creation of suitable language resources.
Hence an important step towards understanding how tobuild dialogue systems for information access to large unstructured text collections is to collect a corpus of dialogues
based on a realistic information-seeking scenario for a lessfocussed task. In this paper we address this issue, describing one such information-seeking scenario, the methodology we have adopted for creating such a corpus and the
corpus that is currently under construction.

2. The Information Seeking Scenario:
Background for Breaking News
Background information gathering in response to a newevent – a scenario exemplified by the activities of journalists assembling background for breaking news stories – isone real world scenario where we find people seeking information from open domain text collections. Although in current practice background information seekers typically relyon standard information retrieval (IR) techniques to accessinformation in text, we believe that this is a scenario which
is of significant interest to dialogue studies. We explain thisposition by first describing the task in the news domain andthe goals of people carrying out the task in more detail andthen by summarising the key factors that, in our opinion,suggest this is a fruitful task for generating informationseeking dialogues of a type which have not yet been studied in detail, are richly interesting as dialogues, and are ofsignificant practical importance.
There are a number of task contexts in which a journalist
might require background. Some of these leave little obvious trace, such as when a journalist is preparing for aninterview or inserting small amounts of background intoa current story (e.g. John Doe, president of FooBar Inc.
and former world tiddlywinks champion, ...). Others leave
clearer traces, such as when, in support of a big story, ajournalist is instructed to prepare a background fact-sheet
to assist other journalists putting the current story in context (for example, a list of previous train crashes), or evento write a dedicated, so-called “backgrounder”, which isan extended prose piece whose function is to contextualise
the current event. In work reported elsewhere, which describes research into the application of Question Answering (QA), summarisation and Information Extraction (IE)
technologies to support background gathering from newsarchives (Saggion et al., 2005), we focussed on the background writing task and carried out interviews with journalists, controlled observation of current practice and text
analysis of around 50 archived backgrounders. This work
has provided a better understanding of the goals of journalists seeking background information from a text archiveand some insights into the nature of the process.
We observed two high level goals, each providing context
for the current event. One is to provide simple descriptive
information for entities and events that figure in the current
news story. For example, in a background to a hurricane
we see the proposition: A storm can only be classified asa hurricane if its wind speed is faster than 73 mph. A second is to find information about past events that can be used
to frame the current event in a narrative that is both compelling and significant to the intended audience. Journalists
describe this second goal as ‘angle seeking’ for the news
story and while they may begin the task not knowing whatthe ‘angle’ may be, they have an expert understanding of
the kind of information that needs to be examined in orderto develop and support an angle. We can identify a numberof types of information which are commonly used to provide angles in a news story, including: (1) chronologicalsequences of events; (2) possible explanations for a particular outcome; (3) interesting associations between groupsof events or entities; (4) extreme or distinguished examplesof similar events or entities; (5) information that places acurrent event or entity in a scale of similar events or entities.
We believe the following characteristics show why this scenario is a fruitful one to use in constructing a corpus of
information-seeking dialogues and what makes it unique.
Discovering new information from text archives: connecting entities and events Given the goal of providing
descriptive information for entities or events we can expectthat background information-seeking dialogues are likely
to include examples of simple factoid question-answering
as well as exploration of unfamilar topics, as described inwork on information-seeking chat (Stede and Schlangen,2004), where the answer information is assumed to be explicit in the knowledge base (text archive, domain model)
used to drive the dialogue.
We can contrast these examples of information-seeking
with one where the information-seeker is looking to discover novel information by making connections between
existing data. Journalists seeking background information
are looking for information that could provide an angle for
their story. While news archives often contain examples ofbackground information to past news events, which mightprovide ready-made associations between events and entities, more commonly the journalist must identify the patterns in the archive himself, i.e. he has to actively makenew associations between events or entities. We are notsure what kind of dialogues this will produce.
Imperfect user model of information source Background information-seeking is carried out against large text
archives. In general a user will have only a poor modelof the sort and extent of the knowledge held in such a resource. As a consequence part of the user’s activity must be
learning about the contents and limits of the resource he issearching against, not just about the topic of interest to him.
This contrasts with information-seeking against structured
databases where the user often has an accurate model of thetype of information held in the database and the database
may be complete with respect to the user’s tasks.

Inherently iterative Because of the previous two characteristics – seeking to discover novel connections and imperfect user model of the information source – backgroundinformation-seeking leads naturally to iterative information
requests as the information-seeker learns about the topic,
discovers connections, and learns about what archive contains. Dialogue is a natural mode of interaction in this situation.
Complex/non-unique answers Unlike factoid QA or
information-seeking against structured databases, background information-seeking is unlikely to result in single,
simple answers. Rather results are likely to be rich collections of related facts and different information-seekers
carrying out the same task are likely to end up with different, but equally valuable results, because there are multiple
possible angles.
Unpredictable nature of dialogues Since backgroundinformation-seeking is a complex task without a precisely
defined result, dialogues realising the process are likely to
be more unpredictable and variable than those realising previously studied information-seeking tasks such as factoid
QA against text collections or question answering against
structured databases.
Limitations of search engine technology Backgroundinformation-seeking is currently supported by conventional
search engine technology. All of the journalists interviewed
in this work have spontaneously voiced discontent withthe limits of search engines for background informationseeking. After participating in the dialogues described
below they were very enthusiastic about the utility of a
dialogue-based interface to text archives that could support
background information-seeking if such a system could be
developed.

3. The Cubreporter Dialogue Scenario
Ideally we wish to capture information-seeking dialogues
between two humans, one of whom has full knowledge ofa news archive. However, no such individual exists. The
closest we get in current practice are telephone conversations between a mobile reporter and a colleague in the newsroom, a “cub reporter”, who acts as his proxy in gathering background, using a search engine over a news archiveor a cuttings library. While this scenario suggests there
is a real requirement for reporters to have remote accessto background information via a spoken dialogue, the resulting dialogues are several steps removed from the sortof dialogues one would expect with an ‘ideal’ information
provider. For example, the time required to get complete information is not feasible given news deadlines and thus theinformation provider typically relies on a handful of texts
and is consequently not in a position to answer follow-on
questions. Furthermore, such dialogues may contain longinterruptions while the provider searches for relevant information or periods where the provider is simply reading
aloud chunks from the archive.
To more fully approximate the situation in which dialogues
are generated between an information provider (IP) who
has fully assimilated the content of an archive and an information gatherer (IG) who is seeking background in theIn the years since the Second World War, Scotland has transformed itself from a nation of coal miners and shipbuilders into a modern, technology-basedsociety. The cornerstone of this society is the Scottish higher education system. Scottish universities produce more graduates in the natural sciences,mathematics and computing on a per capita basis than Japan, the US or any country in Western Europe. Over recent years Scottish firms have been shownto spend far less on research than the UK average. In 1999, the Scottish executive created the Proof of Concept fund as a six-year, pounds 33m initiative.
A funding gap was identified between the research activities in the university laboratory and those of a proven concept in which a commercial investor
becomes interested. The fund, now in its fourth year, currently supports 120 groundbreaking projects worth over 19m. Companies that have started fromprojects receiving Proof of Concept funding include creative media company Virtual Clones Limited, which began life as a Glasgow University research
project. The long-term advantages are two-fold: first, the research will be commercialised to the benefit of Scottish and international businesses. Second,participation in the project will produce a raft of highly skilled, commercially aware Scottish graduates to address the skills gap identified in this sector.
Through these and other initiatives, Scotland is beginning to position itself within the international knowledge economy. Scottish Enterprise, Scotlands
main economic development agency are providing the latest round of funding for the project. They say their key priorities of are to provide a range ofhigh-quality services to:- help new businesses get underway;- support and develop existing businesses;- help people gain the knowledge and skills they will need for tomorrow’s jobs; and- help Scottish businesses develop a strong presence in the global economy, building on Scotland’s reputation as a great place to live, work and do
business.
There were 124 applications for support but only 37 projects are sharing the 5.6m donated in the latest round of Proof of Concept awards. Among them
are a Glasgow University project that screens for lung cancer by monitoring breath, and new third-generation mobile phone technology from RobertGordon University in Aberdeen. Waging war on the common midge and reducing the amount of harmful gases belched out by grass-munching farmanimals are two of the other 35 academic projects that are sharing the money. Meanwhile, Dundee University was granted three awards, worth 550,000
to support its concept for software mimicking human movements; to develop new drugs to cure or prevent potentially fatal fungal infections in cancer
patients; and to develop a complete prototype system for a hearing device implanted in the ear. Abertay University received funding to build and testa software tool, designed to improve the competitiveness of Scottish digital games companies. It will also further develop its technology to speed up
the purification of waste water from textile processing, paper making, food and drink processing and chemical factories. Edinburgh University got fiveawards, worth 950,000, one of which will see it develop a new technology to identify the presence of oil or gas underground, which it hoped would
sustain North Sea oil production. To date the programme has made 120 awards and created 290 jobs. However, The Proof of Concept programme hascome under intense criticism recently, following the collapse of Essient, the flagship first spin-out from Proof of Concept, which called in the liquidatorslast month after failing to raise second-round funding.

Figure 1: Sample background written for the breaking news story in Figure 2
news domain, we developed the following scenario.
A participant becomes expert in a small part of an archive(which we assume to be complete with respect to an information topic under consideration) via a controlled writingtask, i.e. by writing a full backgrounder. We chose this task
as a means to prime the participant for the role of IP, since
writing provides a natural method for organising information and ideas. Having completed this task, the participant
then plays the role of IP in a dialogue with an IG, a person
playing the role of a reporter seeking information on the
topic to inform his coverage of a new event.
We studied this scenario in a pilot experiment, which suggests the method facilitates the production of rich, naturalistic dialogues. We are now building a full corpus consisting of the archive texts used to inform the IPs, the corresponding background ‘summary’ texts produced by theIPs in the controlled writing task, and the dialogues between IPs and IGs. This corpus will allow us to explore research questions such as: (1) how different individuals seekor present information on different topics; (2) how one individual seeks or presents information on different topics;(3) how information presentation varies in style between
speech and text; (4) system requirements for text processing
on the source or background texts to support the behavioursof the information provider; (5) system requirements for a
dialogue system targeting the source or background texts asa data source.

4. Experimental Method
Below we explain in more detail our method for simulating
dialogues for information access to news archives.
4.1. Roles and Participants
We selected participants for the dialogues on the basis ofskills and experience. For the role of IG, we recruited pro-fessional journalists from various media organisations including national news agencies, national broadsheets, regional newspapers, radio and magazines in addition to university lecturers in journalism who have prior experience
working in one or more of the media listed above. Professionals have expertise in researching background for news,
both in the context of writing tasks and in conversation withexperts, colleagues and news archivists. This suggests theyshould comprehend the simulated “background for breaking news scenario” and the role of the IG with little difficulty. Moreover, we asked professionals to participate in
the pilot study and they played the role of IG quite naturally
and with purpose. They typically asked a range of questions
on a topic, explored aspects of background through a seriesof connected questions, and queried new topics that arose.
For the role of information provider we sought participants
with good verbal presentation skills and knowledge of researching and writing backgrounders since this experienceshould help them to (1) provide appropriate and useful responses in background for breaking news dialogues and(2) carry out a controlled background writing task using acollection of pre-selected archive news texts. Professional
journalists, especially those working in broadcast news,
typically have such skills. However, due to the practicalconstraint of obtaining large numbers of professionals, we
asked journalism students with experience in writing news
copy, background news gathering and radio journalism toparticipate in the role of IP.
4.2. Assimilating Information from Text
We prepare a participant for the role of IP in a cubreporter
dialogue by asking them to carry out a controlled writingtask using a small collection of archive texts. By using preselected sources – as opposed to allowing them to search fortheir own texts – we hoped to reduce the time spent by par-

ticipants in the search process and to exercise experimental
control over the information source.
We designed the archive collection with two objectives: (1)to capture some of the characteristics of an open domain
text collection, i.e. to include material from different news
media, genres and topic areas; (2) to provide broad coverage of different types of background content for a given
news story, in order to allow the IPs to view the current
event from different perspectives. To meet these requirements we asked three independent researchers not participating as IPs or IGs to each carry out a lengthy archive
search and to find up to 15 documents which they judged tobe the best coverage of background information for a particular breaking news story (e.g. resignation of a politician,hurricane, etc.) and which would be suitable for use in a
typical news wire backgrounder. To assist them in this task
we provided a summary extract from a news wire story. Resources we used in the searches included the World Wide
Web and specialised news archives, e.g. the Press Association archive, which contains news copy from agencies andnational and regional newspapers. From this initial pool weremoved obvious redundancy (i.e. identical sources) andthen continued to deselect texts, aiming for a total of around25 individual documents which in our judgement provided
a good balance of background ingredients (e.g. material
on different topics over time; definitional information; factoids; similar events; events leading up to the event). In
the final resource we included nine sets of documents fornine news stories, three in each of three topic areas: natural
disasters, company/person profiles; new investments.
We then asked IPs to write a 500 word news wire stylebackgrounder for one of the nine breaking news story using the corresponding prepared text collection. An example
backgrounder written to support the breaking news story
shown in Figure 2 is given in Figure 1. The IP task description included the same news wire summary extract that weused in the searches. Furthermore we provided brief directives to help them to focus their task, in the form of a“news editor’s comments”, e.g. for a story of a new commercial product release we wanted background to form a
profile of the company. We encouraged participants to readeach text briefly and to try and use multiple sources in thewriting task. But otherwise they were free to use content
as they wished. Finally, we wanted to obtain a record of
the sources used in the task. Hence, upon completion, we
instructed participants to indicate which sources they used
for content in the course of writing their backgrounder.
4.3. Dialogue Roles
To each IP-IG pair we provided the details of a breaking
news event and their dialogue roles in our scenario. The
task of the IP was to imagine that he/she was employed ona news desk and, to the best of their ability, provide background information to callers, using the knowledge he/she
had acquired during the writing task. This included both theinformation they had written about in the backgrounder andinformation they had not used but had remembered fromtheir reading of the sources. To help prepare them for thistask, shortly before a dialogue we asked IPs to spend 5-10
minutes reading over their background (written between 2

TASK T WO
New Investment: Proof of Concept FundCall Maggie on the news desk
You are a newly appointed Scottish correspondent who is to cover thereaction to the news of a new round of investment in scientific research in
Scotland. You plan to interview scientists, businessmen and politicians.
Science funding is not a topic you are very familiar with and you needsome basic background, especially information about the recent historyof the funding body and investment in science in Scotland.
Breaking News Summary
Topic: 1 SCOTLAND Funding
Headline: MILLIONS INVESTED IN NEW RESEARCH PROJECTS
Byline: Scottish Press Association
Date: 2004-07-01
Scots scientists have secured millions of pounds of funding for a host of
ground-breaking research projects, it was announced today. A total of
26 projects, from ways to improve cancer detection, tests for glaucoma
and treatment to the development of a fish aerobics programme, have secured a share of a multi-million pound funding pot. The 4.5m Proof ofConcept cash will benefit commercially-focused research projects taking
place in universities, colleges and research institutes around the country.
The latest round of funding, which is backed by Scottish Enterprise andthe Scottish Executive, was announced today by deputy enterprise minister Lewis Macdonald... Todays announcement is the fifth round of support
from the fund... Around 60 research posts will be created as a result...

Figure 2: Sample brief for mobile reporter, the Information
Gatherer (IG)hours and 2 days in advance). We also gave them a copyof the background they had written for reference during thecourse of a dialogue if, for example, they wanted to checkdetails for a fact or jog their memory. However we asked
them to limit their use of the text, and try to avoid readingout loud from the written background as best they could.
For IGs, we prepared an “assignment brief” for the breaking news story they were to imagine they were covering.
See Figure 2. This included details of their job in a news
agency, e.g. “Scottish Correspondent”; details of an action
they were planning to carry out, e.g. “interview scientists,businessmen and politicians”. We also provided comments
to indicate the focus for the story and the same breaking
news story used by the IPs in the background writing task.
Lastly, we asked IGs to initiate the dialogue and to continue to ask for information until he/she either reached thegoal of recovering background for the assignment or believed he/she had exhausted the supply of relevant information from this source. We did not prompt either participant
further since the pilot study suggested rich, mixed-initiative
dialogues occur naturally.
4.4. Corpus Collection
The experimental design allows us to explore informallythe effects of the variables: topic, seeker and provider. The
final corpus will consist of 54 dialogues based on 9 IPs
each writing 3 backgrounds, each of these in a different
topic area (natural disasters, company/person profiles, newinvestments). For each brief two different IGs call eachprovider. An IG typically talks to three IPs, in each case following a background task assignment for a different topic.

5. Corpus Characteristics
There are six types of data in the corpus: task descriptions
for IP and IG; archive source texts; background texts produced by IPs before a dialogue using a subset of the archiveNumber of of sources
Number of words
Number of sentences
Unique words

Total
210
131,278
6,305
56,877

Mean

Min

Max

S.D.

625.1330.02
270.84

70
1
53

7,262
328
1102

616
28.83
155

Mean per topic
70
43,7592,101
18,959Mean per story
23.33
14,586700.6
6,319

Table 1: Statistics of the archive source texts. Unless specified, counts are made over all source texts.

# of words# of sentences
Unique words
Source texts used

Total
8219
447
3957
104

Mean
547.9329.80
263.80
8.73

Min
443
22
191
5

Max
692
38
312
12

S.D.
71.96
5.13
30.51
2.25

lected, and initial analyses of transcripts show that the dialogues have the desired properties (Schegloff, 1982; Clarkand Schaefer, 1987). For example:

S.D.
128.981.73
1.87• Referring expressions and other aspects of meaning
are often collaboratively constructed;

Table 2: Statistics of backgrounders.

Dialogue duration (sec)
Average sources per IP
Overlap between IPs

Mean
412.23
8.5
4.50

Min
207
5.0
3

Max
721
10.5
8

Table 3: Dialogues duration, variation between the averageuse of source texts by IP, and number of overlapping source
texts used between pairs of IPs.
collection; records of the actual sources used in each writing task; spoken dialogue recordings; and full transcriptions of the dialogues.
5.1. Text Corpus
We employed GATE (Cunningham et al., 2002), a suiteof linguistic processors including a tokeniser, a sentence
splitter, a part-of-speech (POS) tagger, and a named entity
recogniser, to perform a quantitative analysis of the sourceand background texts. Table 1 shows details of the source
texts. There are 210 source texts, for a total of 131,278
words (6,305 sentences). There are 23.3 source texts on
average for each topic area available to IPs to write a backgrounder and Table 2 shows that on average 8.7 texts areused per IP to write a story. We have 15 backgrounders andeach of them has approximately 500 words. Table 3 showsthat 4.5 of the source texts used are overlapping among theIPs, suggesting that some source texts are preferred overothers in forming backgrounders.
5.2. Spoken Dialogues
We have collected 30 dialogues, whose duration is on average 6:52 minutes with a standard deviation of 2:09 minutes.
We are in the process of transcribing and analyzing these
dialogues.
A primary motivation with our experimental design was
to collect a corpus of information-seeking conversations
against a textual information source that would have theproperties of real-time interactive dialogue. We particularly
wished to avoid collecting dialogues with: (1) long pauses
in which the IP actively searched for background information in the text archive; and (2) long dialogue segments inwhich the IP read aloud from textual sources. Our pilot
experiment suggested that our experimental design should
achieve this. Observations while the corpus was being col-• There are interruptions, overlapping speech, latchedspeech, other completions, sentence fragments, andfew long pauses;• There is frequent use of pronominals, textual deictics
and other indexical expressions;• The dialogues are mixed initiative. Although the IG
and the IP have particular roles, initiative is passed
between them with the IG taking control and interrupting to pursue particular angles or aspects of theinformation provided, and the IP often volunteering
unasked for information, or answering different but related questions.
We can illustrate some of these characteristics with examples from a dialogue for the task in Figure 2, with the fullbackground text prepared by the IP in Figure 1.
The dialogue excerpt in Figure 3 illustrates iterative questioning and responding, i.e. where the IG interrupts (IG1)
to ask a question about content in IP1. The dialogue segment from IG1 to IG5 shows the collaborative constructionof a referring expression June 2004, where the IP and IG
work together to ascertain the date when a particular event
took place. It also contains a large number of deictics.
IP1:
IG1:
IP2:
IG2:
IP3:
IG3:
IP4:
IG4:
IP5:
IG5:

...but the liquidators were called in fairly recently
When was this, liquidators?
Sorry
When was this?
Well it says last month
Which year, what year was it, this year?
Last month, it’s 2005 now according to this.
So it’s last year I can say?
Yeah so it’s like June 2004
Brilliant so June 2004.

Figure 3: Sample dialogue 1.
Figure 4 illustrates angle seeking by the IG, who wants
to know of any big achievement or project successes following a previous funding award, and later, if any of the
projects have been concerned with ‘animal science’. The
IP responses to each of these requests illustrate cooperative
dialogue. The IP states that she does not have available information (IP1), and she indicates the scope of what she
does know. In IP2, the IP takes the initiative to offer an alternative type of success. Of particular note is the fact that
the IP has generalised over the initial information request,and offered a related proposition from a broader class of
possible answers. A similar demonstration of reasoning via
relaxation to a broader class is shown in IP4, where the IP
acknowledges that a project on midges might not count asanimal science, and a further exchange occurs to clarify inwhat class the project does fall.
IG1:

IP1:
IG2:
IP2:
IG3:
IP3:
IG4:
IP4:

IG5:
IP5:

Uh, okay, what’s their biggest achievement, is there anything about how
they’ve ordered money for anything kind of high profile or have you
found anything, have you found any success attached to...?
No not particularly, I sort of know more about how much money has
been given to various people and what for. As opposed to successes.
Okay.
But I do know one good thing, is that it’s created 290 jobs.
290 jobs in the space of six years, jobs okay.
And 120 projects have had awards over that time.
Oh brilliant, over the six years. Has there been anything to do at all withanimal science?
Animal science, well there’s, I don’t know if they count as animals actually but there is a project against midges, it actually says waging war on
the common midge, and reducing the amount of harmful gas producedby munching farm animals
But it’s not sort of directly animal science
No it’s more sort of environmental I think

Figure 4: Sample dialogue 2.
In Figure 5, the IP takes the initiative (IP3 to IP7), to provide further information on the role of the funding body,speculating on the meaning of the information in her response. There is also a reformulation in IG7, where theIG reframes the conceptualization of the funding body, andgets the IP’s assent. We hope to quantify the extent to whichthese various phenomena occur in future work.
IG1:
IP1:
IG2:
IP2:
IG3:
IP3:
IG4:
IP4:
IG5:
IP5:
IG6:
IP6:
IG7:
IP7:
IG8:
IP8:

Hmmm and what does it, does it kind of dole out cash to scientificbodies so that they can research certain things?
Humm basically yeah what they do err what they do is err bodies anduniversities apply err for money
Yes
For for awards from itOh
Hmmm and then they allocate it, I think they must be allocated depending on what they want to do with the money
I see
Hmmm because for this one there are 124 applications
Ok
But only 37 of the applicants actually got money
Oh right
So they must judge them on something but I don’t know for sure what
they judge them on
So it’s a bit, ok I get you, so it’s like an award body
Exactly
For scientific communities
Yeah

Figure 5: Sample dialogue 3.

6. Conclusion
We have described an experiment to construct a corpus of
naturalistic dialogues between an information-seeker witha partially specified information need and an informationprovider, where the latter is conceived of as having fullknowledge of a large unstructured open domain text collection. In future work we plan to use this corpus to explore:(1) how different individuals seek or present information ondifferent topics; (2) how one individual seeks or presents information on different topics; (3) how information presentation varies in style between speech and text; (4) system requirements for text processing on the source or backgroundtexts to support the behaviours of the information provider;(5) system requirements for a dialogue system targeting thesource or background texts as a data source.

Acknowledgments
This work has been supported by the UK EPSRC Grant
GR/R91465, and a research collaboration grant from NTT
Communication Science Laboratories to the Cognitive Systems Group at University of Sheffield. We are also gratefulto the journalists and journalism students who participatedin our experiment.

7.

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