![](bg6.png)
3.7 Chances for Long-term Interaction
A long-term interaction with an ACC cannot be en-
forced. Potential users of an ACC – the learners – re-
ported that they are curious to chat with the system for
the first time, but they will not keep interacting with
the system if it does not make sense for them. The
goal of the ACC designers is to create necessary con-
ditions to make long-term interaction possible. How-
ever, it cannot be a system requirement to achieve a
dialogue of a particular duration in multiple sessions.
As Examples 1 and 2 show, the interaction parties
are aware of them engaging in a joint activity mean-
ingful for both of them, that this activity will span
multiple sessions, and that after a particular number
of sessions of hopefully pleasant interaction, the ac-
tivity is completed, or its meaning has changed and
they keep interacting. This was not avoidable for the
data collection with volunteers. The interaction with
an ACC does not necessarily have to end after a par-
ticular number of conversations.
We cannot take it for granted that the learners will
accept the ACC as a language expert. The users will
try to find out what the ACC does not understand. The
ACC designers need to anticipate as much as possi-
ble of such scenarios in order to let the machine look
smart or funny, but still polite according to the level
of social closeness.
4 CONCLUSIONS
We collected data from human-human IM dialogues
that revealed valuable patterns for social interaction
and activity in the context of conversational training.
In contrast to most existing Companion prototypes,
we use empirical data for the design of an ACC. We
described how we use these empirical data in order to
satisfy the requirements of conversation, utility, adap-
tivity and long-term interaction.
To effectively use the patterns for the ACC design,
a consistent, holistic rule framework will account for
the interdependencies in recognising patterns in real-
time during the interaction and in producing appropri-
ate system (re-)actions in terms of utterance content
and interaction management.
ACKNOWLEDGEMENTS
We would like to thank all the participants of the data
collection experiment for their voluntary work.
REFERENCES
Avrahami, D. and Hudson, S. (2006). Responsiveness in in-
stant messaging: Predictive models supporting inter-
personal communication. In HCI, pages 731–740.
Boyd, A. (2010). EAGLE: an error-annotated corpus of be-
ginning learner german. In Proc. of LREC. ELRA.
Crysmann, B., Bertomeu, N., Adolphs, P., Flickinger, D.,
and Kl¨uwer, T. (2008). Hybrid processing for gram-
mar and style checking. In Proc. of the 22nd Int. Conf.
on Comp. Linguistics, pages 153–160. Coling.
Danilava, S., Busemann, S., and Schommer, C. (2012).
Artificial Conversational Companion: a requirement
analysis. In Proc. of ICAART, pages 282–289.
Forsythand, E. N. and Martell, C. H. (2007). Lexical and
discourse analysis of online chat dialog. In Proc. of
the International Conference on Semantic Computing,
ICSC ’07, pages 19–26. IEEE Computer Society.
Jia, J. (2009). CSIEC: A computer assisted english learn-
ing chatbot based on textual knowledge and reasoning.
Know.-Based Syst., 22(4):249–255.
Jiang, H. and Singley, K. (2009). Exploring bilingual, task-
oriented, document-centric chat. In Proceedings of
GROUP ’09, pages 229–232.
Kerly, A., Hall, P., and Bull, S. (2007). Bringing chat-
bots into education: Towards natural language nego-
tiation of open learner models. Know.-Based Syst.,
20(2):177–185.
Koch, P. (1994). Schriftlichkeit und sprache. In Schrift
und Schriftlichkeit. Ein interdisziplin¨ares Handbuch
internationaler Forschung, pages 587–604. Walter de
Gruyter.
Koch, P. and Oesterreicher, W. (1985). Sprache der
N¨ahe – Sprache der Distanz. M¨undlichkeit und
Schriftlichkeit im Spannungsfeld von Sprachtheorie
und Sprachgeschichte. In Romanistisches Jahrbuch,
volume 36, pages 15–43. Walter de Gruyter.
Krashen, S. (1981). Second Language Acquisition and Sec-
ond Language Learning. Oxford: Pergamon.
Lin, J. (2012). Automatic Author Profiling of Online Chat
Logs. Kindle Edition.
L¨udeling, A. (2009). Corpus Linguistics: An International
Handbook. Mouton de Gruyter.
L¨udeling, A., Walter, M., Kroymann, E., and Adolphs, P.
(2005). Multi-level error annotation in learner cor-
pora. In Corpus Linguistics.
Nardi, B. A., Whittaker, S., and Bradner, E. (2000). Interac-
tion and outeraction: instant messaging in action. In
Proc. of the ACM conf. on Computer supported coop-
erative work, pages 79–88.
Orthmann, C. (2004). Strukturen der Chat-Kommunikation:
konversationsanalytische Untersuchung eines Kinder-
und Jugendchats. PhD thesis, Freie Universit¨at Berlin.
Rich, E. (1979). User modeling via stereotypes. Cognitive
Science, 3:329–354.
Shawar, B. A. and Atwell, E. (2007). Chatbots: Are they
really useful? LDV-Forum, 22(1):29 – 49.
Solomon, J., Newman, M., and Teasley, S. (2010). Speak-
ing through text: the influence of real-time text on dis-
course and usability in im. In Proc. of the 16th ACM
int.l conf. on Supporting group work, pages 197–200.
ICAART2013-InternationalConferenceonAgentsandArtificialIntelligence
246