8 questions or concerns about what to expect on the day
In example (5), the user asks a question about the MRI scan (l. 1). The
user reacts to the bot’s repair initiation (l. 2-3) by accommodating
through choosing one of the bot’s prompts (l. 4). Whilst the bot’s
answer may not be an altogether satisfactory response to the question,
it concludes this sequence and moves the conversation on.
In contrast, users who use the strategies with a lower success ratio
tend to have to work harder to have their intents understood and ensure
progression:
Example (6)
1 Please select a date and time for your appointment 18:00 19:00
None
2 7pm
3 Sorry, but I could not reschedule the appointment. Please try
again a bit later, or try re-booking
4 with different date/time. If however you are still facing issues
I can connect you with someone
5 from the clinic to help you out, just type - speak to someone.
Thanks, Asa
6 I meant 19:00
7 Did you mean? google *calendar* link for my scheduled
appointment *faq* - frequently asked
8 questions restart *None* of these
9 None of these
10 Apologies, bear with me. I’m still learning. Can you rephrase?
11 Can I reschedule
12 No worries. Please let me know the number of the appointment
you would like to reschedule: 1.
13 Screening on Thu 09 Jun ’22 at 19:00
In example (6), the trouble source is the user’s input representing
their desired appointment time (l. 2). The bot’s response is relevant,
but nevertheless initiates user repair by indicating that the desired
appointment time is not available and providing a number of options for
how the appointment can still be rescheduled (l. 3-5). The user
completes repair by rephrasing their desired appointment time (l. 6).
The user’s repair is wordier than their previous version, with ‘I mean’
representing a personal mitigator akin to a negative politeness marker.
After another comprehension request by the bot (l. 7-8), the user
changes strategy and uses accommodation strategies by recycling one of
the bot prompts (l. 9). When this strategy is also unsuccessful, the
user’s next self-repair repeats the originally rescheduling request,
this time framing the request as conditionally indirect request (‘Can
I…’) (l. 10). This third attempt at user self-repair is leads to
a relevant response but forces the user to restart the rescheduling
conversation.
Discussion
Summary of results
The analysis presented in the previous section has showcased, through a
combination of qualitative and quantitative description of user repair
turn and subsequent bot follow-on turns, how users in a task-oriented
chatbot use repair to work through sequences in which their intent is
misinterpreted by a bot. In the subsequent discussion section, we will
summarize and discuss these insights and discuss them in relation to
developing communicative competence for AI.
The study has shown that the strategies the majority of all instances of
repair – and thus the focus of the study – are other-initiated
self-repairs of repairables in user turns. Hence, the main purpose of
repair is to address issues in which the bot does not understand users’
intent – leading to false negative responses – or misunderstands it –
leading to false positive answers. The study has also shown that the
strategy users deploy most frequently – rephrasing – Is actually one
of the least successful one, leading in approximately a half of all
instances to false negative answers (other repair initiations by the
bot) or false positive answers (irrelevant bot responses). On the
contrary, restating the purpose has a high success ration, but is only
very infrequently deployed by users. In addition, ‘accommodation’ –
using the bot’s own prompts – and making different choices also has a
more than 50% success ratio.
This strongly suggests that users transfer strategies from face to face
spoken interaction into their conversations with Asa. For example, we
have shown that some users not only rephrase their original turns, but
also enhance them with additional politeness markers. This mirrors
strategies previously observed in face to face-interaction. For example,
Kasper (2006) observed speakers that, in subsequent requests,
interviewers in an oral proficiency exam treat conventionally indirect
request frames ad redundant, instead using the politeness marker
‘please’ to frame an overall more direct request. Plug (2014) observed
that, in self-initiated self-repair, speakers engage in “prosodic
marking” through higher pitch, and higher speaking tempo, and Hauser
(2019) observed ‘upgraded’ self-repeated gestures in Japanese
interaction. Whilst the self-repair patterns in this paper contradict
rather than confer with confer with Kasper’s (2006) observations – for
instance, in some instances speakers formulate ‘repaired’ questions in a
conventionally indirect manner – they nevertheless support an overall
pattern of people applying learned social behaviours to their
interactions (see Nass & Moon, 2000).
Whether ‘upgraded’ politeness is a matter of applying behaviours from
face-to-face interaction, or is a result of other demands, remains to be
further investigated by further research. In doing so, the notion of
‘pragmatic transfer’ (Kasper, 1992) which traditionally has been used to
describe the transfer of L1 pragmatic strategies into an L2 and has
informed plethora of research in intercultural, cross-cultural and
interlanguage pragmatic since could be deployed to inform research on
human-AI interaction, too.
The analysis has also revealed further insights which may have
implications for users’ perceived rapport with the bot. As shown,
‘accommodation’ – defined here as convergence to the bot’s prompts in
self-repair is one of the most frequently used, and one of the more
successful strategies, though success is not necessarily guaranteed.
Yet, such ‘upwards convergence’ towards interactional partners in
superordinate positions (Giles & Ogay, 2007) may have implications for
users’ perceptions of rapport with the bot for two reasons. Firstly, the
conversational repair mechanisms observed here ‘role-defining’
(Liebscher & Daily-O’Cain 2003) in that they define users’ role as that
of a respondent to rather than an initiator of interaction. Secondly,
having to converge to receive a relevant answer in the first place, and
such convergence actually often NOT leading to a relevant answer (see
table 4), has the potential for implications for users’ perceptions of
rapport with the bot (Spencer-Oatey, 2008). However, the exact rapport
implications in user-bot interaction need to be the subject of further,
more detailed investigations.
On the other hand, together with ‘restate purpose’, accommodation is the
strategy that has the highest potential success rate. Yet, user
awareness of this strategy and their readiness to use it, may depend on
users’ overall experience with chatbots and their general knowledge of
the workings of AI (Luger & Sellen, 2016). It may also depend on users’
overall orientations to these interactions: To what extent do they
perceive them as ‘relational’ (relationship-oriented), or as
‘transactional’ (task-oriented) (Koester, 2004)? To what extent do they
perceive of them as having human characteristics (anthropomorphism)
(Hermann, 2022)?
Implications for AI skills
As discussed above, we purport here that, in order to engage with
AI-driven chatbots efficiently, users need to acquire specific skills
that don’t necessarily mirror those for spoken interaction. A range of
models of communicative competence have previously been used to describe
the competences required for social interaction. For example, Canale &
Swain (1984) describe communicative competence as consisting of four
components: grammatical competence (words and rules), sociolinguistic
competence (appropriateness), discourse competence (cohesion and
coherence) and strategic competence (appropriate use of communicative
strategies). This analysis of self-repair in user-bot interaction shows
that for effective engagement in these interactions three of these
competences are particularly important:
- Sociolinguistic competence: Users need to be able to be aware of the
sociolinguistic environment they encounter when they interact with the
bot, what this means for how they manage rapport and, consequently,
how they use language. This analysis has revealed that some users may
be confused about what sociolinguistic environment they are working
in: they may address Asa directly or requests and ask questions with
markers of negative politeness which mitigate the force of the
request/question in one turn, but then accommodate to and use the
bot’s short prompts or restate the purpose of the interaction after an
extended repair period in another.
- Discourse competence: Users need to be able to assess how the way they
use language contributes to a coherent whole in user-bot interactions,
and whether different ways of using language may lead to different
outcomes. For example, how does the bot process and respond to a human
request to form an effective question-answer pair? As the analysis of
the user-bot interactions here have shown, to be effective, it is
useful to know which repair strategies are likely to lead to a
relevant response by the bot that shows that the user’s intent has
been understood and forms a coherent adjacency pair with the users’
input.
- Strategic competence: Strategic competence is at the core of
conversational repair as it describes the skills required for
misunderstanding, and for preventing misunderstanding. As our analysis
has shown, while almost none of the repair strategies mentioned has a
100% success rate, some are inherently more successful than others,
and the criteria for success are different to regular face to face
interaction. Our analysis has also shown that conversational
flexibility may be the most important criterion for strategic
competence. Conversational partners need to be able to recognise when
a strategy is not successful, and then dig deep into their
conversational arsenal to identify and then deploy alternative
strategies.
In summary, in the same way as pragmatic competence is not necessarily
developed alongside other aspects of communicative competence (Kasper &
Roever, 2005), communication skills for conversational AI are not innate
to all users (Luger & Sellen, 2016).
This suggests that there may be value in teaching these skills
explicitly. An approach to teaching skills for effective engagement in
conversational AI would benefit from including all of the elements
previously described for the development of communicative competence
(e.g., Jones & Stubbe, 2004 for professional communication; Dippold,
2015 for classroom interaction), such as awareness raising, experiential
learning, reflection. Indeed Howard’s (2012) instructional paradigm for
teaching CMC skills includes all these elements, and Weisz’ (2019)
experimental account of teaching strategies for successful human-agent
interaction does so, too. Their intervention included a phase on raising
users’ empathy with the bot to highlight the difficulties for dealing
with user input. In this study, the instructional intervention led users
to report that they had developed better strategies (e.g., use simple
language, specify intent precisely). Users also developed their
understanding of the algorithmic thinking process and learned to
disambiguate its capabilities from human capabilities. This study is, to
my knowledge, the only one currently that reports on an attempt to
explicitly teach conversational AI skills. More studies which focus on
AI skills ‘in the wild’ rather than an experimental setting would
supplement this work.
AI design implications
The findings of this paper also have implications for AI design and
development. For instance, in ordinary face to face interaction
other-repair can be initiated by partially repeating the trouble source,
often accompanied by a question word (Schegloff et al., 1977). In
English as a lingua franca research, raising the explicitness of talk –
described by Mauranen (2007) as ‘explicitness strategy’ – has
consistently been observed to be an interactional strategy between
speakers of different varieties of English. These also include,
according to Mauranen (2006), asking specific questions, e.g., after
lack of comprehension of lexical items, repetition of problematic items
in order to elicit some form of explanation. In contrast, Asa the bot
initiates repair by simply indicating lack of understanding without
pinpointing what was misunderstood (see, for instance, example 1).
Raising the explicitness of other-initiated repair may lessen the
ambiguity of repair-initiations for users. However, implications for
face and rapport need further consideration as “explicitly
acknowledging a mistake lowers the likability and perceived intelligence
of the agent and may add friction to the interaction as the user is
obliged to respond to the initiation” (Ashktorab et al., 2019, p. 3).
In addition, bot development could benefit by using user self-repair in
more meaningful ways to further develop the bot’s understanding for
understanding conversational intent. For example, self-initiated
self-repair by users of English as a lingua franca has been described as
a strategy to prevent misunderstanding by raising the explicitness of
talk (Kaur, 2012). Of course, user self-repair is rarely in the open –
in the data reported on above, there were only ten instances in total
(see table 4, no response for illustration) – occurring more commonly
when users edit their turns before they press enter. I propose that
algorithms which record and learn from user self-editing might have the
potential to provide insights into users’ intents. The same also applies
to user self-repair after other-initiation: our data have shown
sometimes extensively long sequences in which users ‘repair’ the intent
multiple times. I propose that the accumulated information from these
self-repairs could be used to build a more complete picture of user’
intent rather than interpreting just one user turn at a time. This
proposal is supported by Li (2020) who purports that “given how hard it
is (even for humans) to correctly detect the meaning of a ‘broken’
message by reading ether half in isolation, we recommend that chatbot
developers consider having their agents process multiple messages at a
time, rather than responding so quickly” (p. 9). This would also be
able to better account for the co-construction and joint mentalizing
that is common to human-human interaction (Kopp & Krämer, 2021), a
concept that has yet to be applied to the development of bot
interactions.
Finally, the insights gained through this project also suggest that, as
users are unsure about the sociolinguistic environment they are
operating in, the workings of AI-driven discourse should be made clear
to users. As discussed above, Weisz et al (2019) used this to teach
experimental participants about the workings of machine learning
algorithms to interpret intent. In practice, a short explanation at the
outset of an interaction could make explicit to users the ‘rules of the
game’ that appear rather opaque to many. This includes, for example, how
to use the bot’s prompts, how to ‘reset’ a conversation (as in example
3). Making the rules of the game explicit to users could prevent
misunderstanding before it occurs.
Finally, conversational AI systems could also be designed to recognise
users’ orientations towards the discourse from participants’ own input.
For example, direct forms of address directed at the bot are potentially
indicative of a more anthropomorphic orientation by which users orient
to the agent as humanlike (Waytz et al., 2010). Such an orientation
might imbue the users with more difficulties in adapting the styles of
interaction which lead to satisfactory international outcomes, but are
less akin to face-to-face interaction (e.g., restating purpose,
accommodation). Using user input to come to conclusions about their
anthropomorphic or dehumanised orientations towards the bot will allow
for conversations to be designed in a way to recognise users who might
more support in their interactions than others.
Limitations
This study has a number of limitations. Firstly, the data analysed here
were gathered as part of a methodological pilot project with a bot
product that was in a ‘minimal viable product’ stage of its development.
The bot responses here therefore do not necessarily reflect its full
capability for dealing with user intents and initiating repair.
Finally, the data set this paper is based on is a relatively small
corpus of user-bot interactions. Further research with a larger data set
is necessary to further substantiate the patterns reported here. This
should, ideally, also include gathering data ‘in the wild’ rather than a
simulated environment. When more is at stage, users’ repair strategies
may be different, e.g., for how many turns they persist in attempting
repair or what strategies they choose.
Nevertheless, the analysis has provided insights which follow-up
research can now further evaluate. For instance, further research could
further investigate whether and how speakers transfer pragmatic
strategies from spoken interaction into bot interactions. Secondly,
further research can ask how users’ repair strategies relate to their
expectations for rapport and their ideologies of language use.
Retrospective interviews asking in detail about users’ decision-making
processes when interacting with chatbots have the potential to be able
to tap into these questions.
In addition, it is of course also necessary to widen our gaze beyond
repair. Informal observations during my analysis suggest that user
success in transactional bot interactions also depends on other factors.
Many of the repairables for instances were longer, discursive sentences.
Are alternative formulations less likely to require repair in the first
place? What do users make request (e.g., their use of politeness
strategies), and how is further progress (e.g., in a booking process)
contingent on its form?
In this research, this paper investigated repair strategies in a chatbot
whose main purpose was ‘transactional’, and which fulfilled tasks such
as booking and rescheduling. Further research needs to investigate the
users’ repair strategies in interactions with bots with a more social
orientation, e.g., bots which provide mental health support or
companionship.
Conclusion
Through the example of conversational repair, this paper has shown that
some users are able to navigate the communication challenges posed in
this environment very well, while some struggle working towards the
transactional goals that the bot is meant to help them achieve. Talking
with a chatbot requires a distinct skills-set, and these skills are
likely to be even more complex in chatbots which use voice interaction.
Further studies on the pragmatics of social interaction with chatbots
will help build the “bigger picture” of what these skills are and need
to be followed up with studies investigating how these skills can best
be taught. Developing users’ knowledge about the workings of AI will
ensure that all AI users have equitable access to the everyday services
bots support.