Double Topic Shifts in Open Domain Conversations:
Natural Language Interface for a Wikipedia-based Robot Application

Kristiina Jokinen
University of Tartu, Estonia
University of Helsinki, Finland
kristiina.jokinen@helsinki.fi

Graham Wilcock
CDM Interact, Finland
University of Helsinki, Finland
gw@cdminteract.com

Abstract
The paper describes topic shifting in dialogues with a robot that provides information from Wikipedia. The work focuses on a double topical construction of dialogue coherence which refers to
discourse coherence on two levels: the evolution of dialogue topics via the interaction between
the user and the robot system, and the creation of discourse topics via the content of the Wikipedia article itself. The user selects topics that are of interest to her, and the system builds a listof potential topics, anticipated to be the next topic, by the links in the article and by the keywords
extracted from the article. The described system deals with Wikipedia articles, but could easily
be adapted to other digital information providing systems.

1

Introduction

Smooth human-like interactions to access data in knowledge bases have long been possible with advanced interaction technology. Current applications combine speech and language technology in mobile
applications such as Siri, Cortana, and Alexa, to allow chat-like conversations related to topics that areof interest to the user, and the aim is to equip such interactive agents with more knowledge for enabling
information exchange with wider topic domains and richer semantics. As argued by McTear et al.
(2016), there is a big need for speech-based conversational interfaces that allow easy-to-use, natural,
affective, and adaptable interactions with the user, while Jokinen (2009) pointed out that speech makes
interactions more human-like and thus increases expectations about the system’s competence in naturalinteraction. One of the bottlenecks for interactive systems has been the amount of data needed for successful interaction management, and usually systems have dealt with limited domains (bus timetables,
flight information, pizza ordering, etc.) where the type and amount of knowledge allows the dialogues
to be manually structured for the purposes of the task and the intentions of the user.
Robots and virtual agents have made conversational AI agents common and useful for various tasks
where interaction with the user is needed. Their human-like appearance calls for more human-like communication, and research has focused on social interaction, multimodal issues, and affective computing
for companion applications. Recently, chat systems or non-goal-oriented dialogue systems have also
received much attention as they seem to encourage entertaining and engaging interactions as well as
provide useful research platforms for studying emotion, social communication, and shared context. For
instance, Zhou et al. (2016) describe evaluation of user engagement in a chatbot system, while Otsuka
et al. (2016) study responses based on discourse relations.
While earlier work on dialogue systems was based on small, manually structured knowledge bases,
we can now take advantage of very large internet-based resources and recent advances in machine learning to train robust interactive information-providing systems. Larger knowledge bases also open up possibilities for systems that are no longer restricted to one particular domain, and enable a move towards
This work is licensed under a Creative Commons Attribution 4.0 International Licence.
Licence details: http://creativecommons.org/licenses/by/4.0/

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Proceedings of the Open Knowledge Base and Question Answering (OKBQA) Workshop,
pages 59–66, Osaka, Japan, December 11 2016.

open-domain conversational systems which use the knowledge sources to provide chat-type interactions
with no dialogue task other than being sufficiently entertaining. However, such open-domain conversational systems are challenging since meaningful interaction also needs to be addressed in terms of dialogue coherence. As argued in Jokinen (2009), conversations are not just a collection of separate questions and suitable answers, but form a conversational thread which exhibits the speaker’s intentions ofwhat they want to talk about (topics) as well as coherent and cooperative interaction management. Using
the terminology of Grosz and Sidner’s (1986) seminal model of discourse structure, dialogue coherence
should be formulated in terms of the user’s intention, attention, and linguistic processing levels.
Our work has focussed on building an interactive robot agent which converses with the user on thebasis of the information found on internet web pages. The WikiTalk application (Wilcock, 2012; Jokinen
and Wilcock, 2014) looks at conversational activity from the point of view of constructing a sharedcontext in which the interlocutors exchange messages about interesting topics. The interlocutors’ activities concern their reaction to the partner’s presentation of new information, and various conversational
management strategies which aim to catch the partner's attention, to build mutual understanding, and tokeep the flow of information going. We hypothesise that this can be best done via a double topicalconstruction of dialogue coherence which refers to discourse coherence taken into consideration on twolevels; the evolution of dialogue topic via the interaction between the user and the robot system, and thecreation of discourse topics via the content of the digital information article itself.
In this paper, we investigate mechanisms for topic introduction in conversational interactions with ahumanoid robot, and discuss models for the computational management of the use of the web as thesource of information through which a robotic agent can draw its “knowledge” for the interactions. The
work extends open-domain dialogue management towards creating dialogues from any web content, but
it also focusses the system on a particular goal in the same way as task-oriented dialogues: here the goal
is to provide useful information based on existing web content and to help users to navigate and find themost interesting web pages with topics relevant to their individual interests. The paper is structured asfollows. We discuss background for our work and present the problem in Section 2, then continue withthe system overview in Section 3, discussion in Section 4, and finally conclude in Section 5.

2

Wikipedia and dialogue topics

In dialogue system design, one of the important issues is to equip the system with appropriate and sufficient domain information. This determines the type of questions the user can ask and the details of
information that the system can talk about. The information needed for dialogue systems often alreadyexists in some form, usually as a website, and the question is how to use this information for creating
dialogues. In this paper, the first steps are described for transforming information automatically fromwebsites into a natural language dialogue which can be used in building a robot dialogue system.
The focus is on how to present information in a manner that allows the user to follow the presentation
and allows the system to anticipate the questions that the user may ask. It is important to notice that
although our goal is to build an open-domain spoken dialogue system, we do not aim at a QA-typesystem that answers questions but rather at a chat-type dialogue system that can follow the user’s topic
shifts. Open-domain QA systems, such as IBM’s Watson (Ferrucci, 2012), use sophisticated machinelearning techniques, question classifiers, search engines, ontologies, summarization, and answer extraction to enable efficient and accurate responses, but the aim of the system is still to find the correct answer
to the question, not to hold a conversation about the topic as such. Interaction development has brought
QA systems closer to dialogue systems, e.g. the RITEL system (Rosset et al., 2006) has a QA component
which is used to ask clarification questions. However, QA systems are still intended to function primarily as interactive interfaces to information retrieval tasks rather than as conversational companions (seeMoriceau et al. (2009) for an overview of information retrieval and automatic summarization systems).
In the WikiTalk application (Jokinen and Wilcock, 2014) the user can have a dialogue with the robot
in which the robot talks ﬂuently about an unlimited range of topics using information from Wikipedia.
The system does not have typical task goals (book a hotel, get timetable information etc.), and is not atypical QA system that provides answers to particular questions. Rather, it aims to function on a moregeneral conversational level and achieve the goal of “provide information on interesting topics” (as longas the user is interested in hearing about it, or the user can switch to a new interesting topic). It is thus
important that the system can anticipate what the possible interesting continuations of the current topic

60

are, i.e. what kind of topical interests the Wikipedia article may bring forward. Related topics that theuser may wish to continue the dialogue with are marked in Wikipedia with hyperlinks to other entries,
so anticipated smooth topic shifts in conversations can be made to the relevant topics via the links.
Following the WikiTalk model, the user can query Wikipedia via the robot and have informationfrom chosen articles read out by the robot. Wikipedia articles are considered as possible topics that the
robot can talk about, while each link in an article is treated as new information that the user can shift
their attention to, and ask for more information about. The paragraphs in the article are regarded aspieces of information that structure the main topic into subtopics, and they form the minimal units forpresentation, i.e. a paragraph can be presented in one ‘utterance’ by the robot. A humanoid robot withmovable arms and legs can also add non-verbal cues to enhance comprehension and to help the user torecognise the discourse level organisation of the text. We experimented with various gestures to provide
structuring for the robot presentation, e.g. the robot uses gestures to emphasise the links while reading
the text without recourse to explicit link menus, changes posture to mark turn-taking, and pauses after
each paragraph to elicit feedback from the user whether to continue on the current topic or not.
In a spoken application, the anticipation of the topics that the user may want to know more about isimportant in order to assist the speech recognition component to arrive at the correct topical word. The
existing method is simply to collect the links of the Wikipedia article and use these as the list of anticipated topics. Coherence of the interaction is thus based on the existing structure of the article and thelinks between the articles: they form the first topical construction of dialogue coherence.
However, the Wikipedia articles may also contain topics which are not currently linked to any otherWikipedia article, i.e. the wikification (Milne and Witten, 2008) of the author’s text has not included
these concepts in the set of linked concepts, or the article itself brings into the mind of the user topics
which are triggered on the basis of the text but are not in the list of links. Our work in this paper addresses
exactly this problem: how to anticipate suitable topics for the human-robot interaction when the topics
are not explicitly marked in the wikification of the Wikipedia articles. Moreover, if the robot system
also needs to relate to other digital resources than Wikipedia, e.g. digital news repositories or otherwebpages, then a more general anticipation method is necessary, since these resources may not have
Wikipedia-type links available for smooth topic shift anticipation. The keyword extraction method tobe described below is an alternative method to identify topics and forms the second topical constructionof dialogue coherence. We call the two processes double topical construction of dialogue coherence as
it includes two different but inter-related sets of topics to be created and managed by the system.

3

Topic anticipation

3.1

System overview

The WikiTalk system overview is given in Figure 1. The interaction with the user is handled by Conversation Manager which uses dialogue state representations to describe the current state of the conversation, and executes domain-independent dialogue tasks such as informing, requesting more information, clarifying speech input, and giving feedback.

Figure 1 System overview (from Jokinen and Wilcock, 2014).

61

Conversation Manager receives possible topics and information about Wikipedia pages from Wikipedia Manager which takes care of the Wikipedia interface. The Nao Manager (called so because therobot platform is the Nao robot from Aldebaran) then renders the request via speech and gesture acts.
The domain-specific knowledge is provided through a topic tree (Jokinen et al., 1998; see Section 4),
which suggests smooth topic shifts for the conversation. A topic tree is a structure built on the domain
knowledge contained in the Wikipedia article. Domain-specific topics are represented by the keywords
which describe the content of the topical article, and they form sub-nodes of the current topic node.
The system thus receives two types of possible topics: the links provided in the Wikipedia article
itself as well as the keywords produced on the basis of the domain knowledge. Coherence of interaction
management can thus rely on the new information provided either by the logic of the wikification of theconcepts in the topical article, or by the information content of the article represented by keywords. In
this manner interaction management is extended to cover various types of open-domain topics that Wikipedia gives rise to, automatically and without manual annotation, and the interaction between the robot
and the user can be made topically richer and more natural.
3.2

Topic anticipation

The topic tree is built by the Topic Anticipation module which is part of Wikipedia Manager. It selects
the keywords from the Wikipedia article using standard keyword extraction techniques. Figure 2 presents an overview of the system following Jokinen and Mikulas (2016), where the algorithm is describedin more detail.

Question
generation

Conversation
Manager

Figure 2 An overview of the topic anticipation module (following Jokinen and Mikulas 2016).

62

The algorithm first selects relevant sections from the web pages, then extracts text paragraphs fromthe sections, and cleans up the text before keyword extraction. The extracted keywords are used in question generation, i.e. suitable questions are generated based on the keywords.
Given a webpage like that in Figure 3 about Shakespeare, keywords are extracted from the paragraphs,
and they represent a simple estimation of the page content. The keywords are determined via a Naïve
Bayesian Filter, following Mooney (2005) and Matsuoka (2003). The frequency calculations currently
use text corpora from Project Gutenberg, but we also plan to use larger corpora such as British National
Corpus and Google Book Ngram Corpus (Lin et al. 2012) to get more balanced scores with respect tothe text genre. The ratio fp/fs i.e. relative frequency of a word in the sample data (fs) and in eachextracted paragraph (fp) measures how frequently a word occurs in the paragraph relative to the normal
sample. The best n words are selected as keywords and in our experiments, the value 4 seems to provide
the best balance between accurate content representations while also being small enough a number forquestion generation. By varying n, it is possible to experiment with a small vs. large number of possible
keywords, i.e. vary the range of possible topics available for a conversation. In order to optimise theresults for dialogue interactions, pruning may be necessary to select appropriate alternatives that accord
with the users’ preferred questions, or machine learning may be used to learn the user preferences.

Figure 3 Wikipedia article about Shakespeare.

Some of the keywords extracted from the Wikipedia webpage about Shakespeare in Figure 3 are'Stratford', 'Hathaway', 'London', 'tragedies', 'Macbeth', 'comedies', and 'romances', which are plausible
next topics: places of residence, wife’s name, and drama genres. Their scores are:Stratford
Hathaway
Londontragedies

2274900.0
1137450.01137450.01137450.0

Macbethromances
later
death

1137450.01137450.0
18957.5
7109.1

In addition to anticipating the next topics in the conversation, the system’s interaction strategies include
anticipation of possible user questions to which appropriate answers should be provided. The extracted
keywords can thus be used for generating questions which are expected to match the user’s interest andinclude possible next topics that the user can request information about. For instance, in the above
Shakespeare example, if the user wants to continue this topic, it is likely that she would like to know
more about Stratford or Hathaway or Shakespeare’s plays. The keywords thus function as links to Wikipedia articles which are suitable answers to such questions, either because of a direct match with anarticle title or via keywords related to the article content. The system described by Jokinen and Mikulas(2016) also has a list of template questions which can be used to produce questions by replacing the63placeholder with a keyword. A template Tell me about X would thus generate requests like Tell me about
Stratford or Tell me about Macbeth. The templates can be used to help speech recognition to recognize
anticipated topics, and if the user then utters such a request, the system can map the keyword to a relevant
website.
3.3

Anticipating user interest in the extracted topics

An important property of a humanoid robot is its situatedness: the robot acts in the same space with theuser, and this contributes to the immediate presence of the robot in the situation. Although robot reactions may be slow, this is similar to face-to-face human interactions where the listener can immediately
give feedback on the presented information and the speaker can modify the presentation according to
the listener’s feedback. The speaker’s anticipation of the partner’s reaction as well as the listener’s attendance to the speaker’s presentation appear as co-creation of the discourse, and are manifested e.g. inproducing feedback in the form of back-channelling and various non-verbal signals. We believe theTopic Anticipation module supports the above view of interaction: it operates both in the perception andgeneration phase, simultaneously as the robot tells the information to the user and observes their reaction
to the presented information.
When developing systems that can talk about interesting topics with the user, a crucial factor is toassess the level of interest of the user. There are two sides to this: first, how to detect whether the partneris interested in the topic or not, and second, what should the system do based on this feedback The
detection of the user’s interest level belongs to the system’s external dialogue interface and includes
interpretation of the user’s verbal and non-verbal feedback signals such as intonation, laughing, eyegaze, nodding, and body movements, to assess her engagement in the interaction (see e.g. Jokinen andWilcock (2012) and references therein). The decision about how to react to the various degrees of user
engagement is part of the system’s internal dialogue management, and how this is done is discussed e.g.
in Jokinen & Wilcock (2014) and references therein. The interest level is specific to a particular topic,
and may change in time. The user may show low interest in the current topic itself, but may show greaterinterest in a piece of new information that is mentioned.

4

Discussion

In dialogue management, topics are usually managed by a stack, which allows a convenient last-in-firstout mechanism to handle topics that have been recently talked about. However, the challenge in managing Wikipedia-based topics is how to convey the Wikipedia structure to the user so that the user cannavigate within the new information links and refer to the content of the Wikipedia article she may wantto know more about.
We use topic trees (cf. McCoy and Cheng 1990) in which topics are structured into a tree that enables
more flexible management of the recent topics than a stack. Topic refers to the particular issue (Wikipedia article) that the speakers are talking about, and NewInfo is the part of the message that is new inthe context of the current Topic (the paragraphs as the robot is reading the text, as well as the links inthe article, and the extracted keywords).
Earlier research (as far as we know) has not been concerned with this kind of double topical construction of dialogue coherence, and we believe the work described in this paper is novel in that it tries
to combine two topical structures: the development of human-robot interaction as coherent topic chainscreated through the interaction, and the recurring sentential topics that make the Wikipedia texts coherent as discourse. The computational management of the two topic structures and their development are
taken care of by the two models: the “traditional” dialogue model is based on the user's interest in aparticular topic and is responsible for driving the conversation forward with dialogue acts such as Question and Inform, while the discourse level possibility to create new topics through the lexico-referential
topical progression is taken care of by the novel Topic Anticipation component performing keyword
extraction. The meaningfulness of the whole interaction is thus built by anticipating possible topicalquestions via the links and via the extracted keywords, and then by the users’ actually occurring choicesof topics that they find interesting.
The dialogue coherence appears straightforward: we can rely on the link structure of Wikipedia toprovide coherence for the dialogue, but also assume that the keyword extraction provides coherence forthe possible continuation of the current topic to one of the keywords. It must be noted that Topic trees

64created by the keywords and from user navigation via links from one Wikipedia page to another provide
a different topic structure from the linguistically oriented topic structure formed from the sentences ofthe Wikipedia texts. For instance, in the above example of the Shakespeare text, the sentential subjects
encode the recurrent topic (Shakespeare) of the paragraphs (subtopics) either directly or through lexical
reiteration, superordination, meronymy, or co-reference. The point of departure chosen by the article’s
writer determines the discourse thematic position of these topics, and all other sentential topics are presented as hierarchically subordinate to it (e.g. surviving works, Stratford-on-Avon, Anne Hathaway,
early plays, tragedies, etc.). The discourse topic in the webpage itself is constructed through the written
coherent text, via lexico-referential topical progression, and it is a different process from the humanrobot interaction concerning the robot telling the user about interesting topics.

5

Future work and conclusion

The paper has addressed issues related to double topical construction of dialogue coherence in the context of WikiTalk, an interactive robot interface to large digital information resources in the internet. The
solution uses natural language processing methods to create automatically a list of possible topics forthe robot to continue a coherent dialogue, on the basis of the webpage associated with the current topic
of the conversation. The purpose of the work is to extend the system’s current method, which uses the
explicitly marked Wikipedia links to anticipate smooth topic shifts, with a new capability to anticipate
topics which are not linked to another webpage, but which may still be interesting to the user based on
the theme of the current webpage.
The work provides another viable avenue to integrate natural language interfaces to novel technological devices like robots, and uses the WikiTalk model to allow access to large digital resources in theinternet. Compared with smartphones, tablets, smart watches, etc., a conversational robot interface features more human language properties which can be expected to make the query interface easier, more
acceptable and accessible. For instance, autonomous robots can move and follow the user independently,
rather than be carried in one’s hand. This allows people who cannot hold or operate a small device intheir hands to talk and hear about the topics they are interested in. Moreover, situated interactions enablemultimodal communication which not only provides alternative ways to access the data, but encourages
holistic communication between the user and the agent.
In many practical applications, new challenges appear for coordinating and managing online information with the help of natural conversation (e.g. teaching, meetings, non-goal-oriented conversations).
Interaction with such applications requires dynamic tracking of dialogue topics and the user’s focus ofattention with respect to their interests and the actual situation. Thus models and techniques for tracking
topics and focus of attention are important, and call for multidisciplinary approaches that combine interaction technology, AI-based system development, and communication studies.
Although the agent’s communicative capability can become livelier and push natural language technology forward, the algorithms and methods still need further improvement and testing. For instance,
the keyword selection could be elaborated and pruning of the keywords for final application be basedon machine learning. Future work will also include more extensive evaluation with the robot agent. The
current system has only been evaluated with respect to its operation and first impressions by the users,
but a more systematic user study is scheduled to be conducted focussing on a system with keywords
selected “as is” and keywords pruned for the purposes of interaction. The evaluation of the topic modelin a practical application will also enable assessment of the effect of the humanoid robot’s appearance
on the user’s experience and evaluation of the system, and whether the robot is able to capture the user’s
attention and contribute to their understanding and topic structuring by its own non-verbal signalling.
A demonstration of the robot interaction will be presented at the main conference (Wilcock et al,2016) to substantiate the sketch of the dialogue interaction presented here.

Acknowledgements
We thank Mikulas Muron for his work on keyword extraction, and financial support from the Estonian
Science Foundation project IUT 20-56 and the Academy of Finland grant n°289985.

65

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