Effective Social Chatbot Strategies for Increasing User Initiative (2021)

Abstract: Many existing chatbots do not effectively support mixed initiative, forcing their users to either respond passively or lead constantly. We seek to improve this experience by introducing new mechanisms to encourage user initiative in social chatbot conversations. Since user initiative in this setting is distinct from initiative in human-human or task-oriented dialogue, we first propose a new definition that accounts for the unique behaviors users take in this context. Drawing from linguistics, we propose three mechanisms to promote user initiative: back-channeling, personal disclosure, and replacing questions with statements. We show that simple automatic metrics of utterance length, number of noun phrases, and diversity of user responses correlate with human judgement of initiative. Finally, we use these metrics to suggest that these strategies do result in statistically significant increases in user initiative, where frequent, but not excessive, back-channeling is the most effective strategy.

## introduction chatbots serve in a broad range of roles, from taskbased assistants to social companions. however, the experience of dialogue agents differs starkly from human interaction. one key difference is how initiative, or conversational control, is shared. in human-human dialogue, participants take turns leading the conversation and introducing new topics @xcite , but in most humanbot dialogue the bot either leads unilaterally (social chatbots) or responds passively (task-oriented assistants). socially, it is unpleasant and unengaging to be responsible for all topic selection and equally undesirable to have no agency. however, these one-sided experiences persist as a natural consequence of rule-based bot design, which only works well when user intents are narrowly confined. with advances in neurally generated dialogue, agents so, you're taking a government course? uh-huh at the university? at tarrant county junior college. oh yeah what are you interested in? dogs. i was wondering what your opinion was, do you like dogs? yes. me too! what about cats, do you like cats? talk to me about movies. we don't have a theater in the cloud, but i love movies. have you seen any good movies recently? i like frozen 2 frozen 2 is a great film. what did you like about frozen 2? elsa okay it's really enjoyable. they asked us if we think it's possible to make laws that eliminate all corruption. i don't think we can make laws to eliminate anything totally. you can make laws against corruption yeah but that doesn't eliminate it, does it? uh-huh i don't think making laws will stop it. i heard that idina menzel played elsa in frozen 2. would you like to hear more about idina menzel? who is idina menzel? statement back-channel positive answer command question alice bob human bot figure 1 : in human-human dialogue foot_0 (left), bob first takes initiative by asking a question, and then uses backchannels to encourage alice to take initiative, which she does by introducing a new topic: corruption. in a typical current human-bot dialogue foot_1 , the bot has initiative and the user responds passively and compliantly, except when interjecting to give a command or ask a question. do you have any pets? yes, i have a dog. ## rethinking initiative initiative is a participant's degree of control at a given moment. consider two dialogue settings with markedly different patterns of control: the cocktail party and the job interview (figure 2 ). at a cocktail party participants share the agency to direct the conversation and take initiative in turns, whereas the interviewer takes initiative throughout the interview and retains control of the conversation's direction. in human-bot social conversation, a user who steers the conversation by suggesting new topics has high initiative, whereas one who follows the bot's lead has low initiative. we examine ideas from prior work on human-human (section 2.1) and taskoriented human-bot (section 2.2) conversation and build upon them to offer a novel definition (section 2.3) of initiative in human-bot social conversation. ## mechanisms of initiative our goal is to improve the quality and naturalness of social bot conversations by enabling and encouraging the user to take greater initiative. we study three mechanisms for increasing user initiative: statements, back-channels, and personal disclosure. statements. in human dialogues, utterance type predicts shifts of control @xcite . we focus in particular on the effect of statements. when codifying changes in initiative, @xcite define four utterance categories: questions, assertions (declarative, factual statements), commands, and prompts (utterances without propositional content, e.g. "uh huh"). whittaker and stenton propose control rules based on these categories. notably, the schemas of both @xcite and @xcite do not consider a control shift to take place if the listener is responding compliantly to the speaker's question, since the question is controlling the conversation's direction. @xcite associates similar actions with a change in control. he gives six "turn-yielding signals," which are behavioral cues from the speaker to the listener that the listener should start talking. of these signals, four out of six cannot be replicated on our bot, since they depend on dialogue features that our bot neither gives, nor receives: pitch, intonation, and body language. the remaining two are trailing off sequences, such as "you know" and syntactic completion of a grammatical clause. it follows from the conclusions of @xcite and @xcite ) that while both statements and questions cue the user to take a turn, statements alone truly provide them with the opportunity for initiative on that turn. back-channeling. in addition to statements, we study back-channels as a signal that the user should take initiative. duncan distinguishes turn-yielding signals from back-channels. since they do not introduce new content, back-channels do not constitute a turn @xcite . instead, duncan finds that they are used by the listener to signal that the speaker should continue. turn-yielding signals, which tell the listener to begin speaking, trigger a change in speaker, while back-channels do not. @xcite also observe that back-channels are used by one participant to give control to the other. however, @xcite ) frame this slightly differently, with control transferring from the speaker to the listener. simultaneous back-channelling is a central marker of shifting control in human-human conversations. however, chat bots cannot perfectly replicate this behavior due to technical limitations which allow only one speaker at a time. personal disclosure. the final mechanism we study is the use of personal self-disclosure as a means for increasing user participation. in human-human conversations, self-disclosure not only increases connection, but produces "disclosure-reciprocity effect": when one participant discloses, the other is more likely to disclose as well @xcite . this effect has also been measured in human-bot conversations. chatbot self-disclosure encourages users to share more about themselves than they would otherwise @xcite . increasing this behavior increases user control, since sharing information without an explicit prompt is a form of initiative @xcite . figure 3 's medium level gives an example of how greater user sharing increases initiative. ## our bot we conducted our experiments using an alexa prize competition bot @xcite . a user saying "let's chat" to an alexa device is randomly connected to one of the bots participating in the competition. to protect user privacy, teams receive user utterances as text only, so we could not leverage the additional signals, such as intonation, that are present in audio recordings. explicit evaluation is limited to a single and optional likert-scale rating at the end of the conversation. alexa prize likert ratings have been shown to be noisy @xcite ; however, the competition rules prevent introducing more fine-grained evaluation questions. instead, we use other automated metrics, as described in section 6. our bot has a modular design, which allows us to restrict our experiments to the modules that are most compatible. specifically, these are the modules that are partially or entirely neural, such as our neural chit-chat module, since they are more flexible to changing user behaviors. amazon user data is confidential, so dialogues shown in this paper are taken from the authors' interactions with the bot. they are representative of typical user conversations, based on an extensive survey of conversation transcripts. ## experiment design and setup we conduct four experiments in our bot, studying the effects of combining statements and questions, using personal disclosure, removing questions from responses, and back-channeling. ## evaluation although human evaluation can provide high levels of detail and accuracy, it is not scalable. this makes it an impractical method for analyzing large-scale conversational data. we therefore propose and validate a set of automated metrics as a good proxy for our levels of intiative. to evaluate our hypotheses (section 5), we use several different metrics indicative of user initiative: user utterance length, number of noun phrases in the user utterance, and negative log likelihood of responses. we validated our metrics on a hand-labeled set of user conversations, see section 6.2. ## discussion our goal in experimenting with initiative was to create a more human-like and engaging experience, in which the user had greater agency to direct the conversation. our results, using both validated automated metrics and manual evaluation (see figures 4 , 5 , 7, and 8), show it is possible to encourage the user to share more information by using linguistic cues. these findings suggest that when given the opportunity, many users will choose to take initiative rather than continuing passively. alexa prize likert ratings are noisy and a poor proxy for overall satisfaction @xcite . since alexa prize evaluation is strictly limited to this rating, we were unable to ask more nuanced questions about initiative from the user's point of view and were unable to directly measure improvement in user experience. while we did find a slight reduction in average ratings as we omitted questions (the only experiment affecting large portions of the bot), this result is likely confounded by the particular experience of our bot. as we see in figure 7 , omitting questions leads users to take higher initiative by suggesting topics or asking questions; however, our bot was not initially designed for this behavior and it is likely that it performed worse on these new types of inputs. we studied whether changing one of the bot's utterances affected the subsequent user response; however, we did not study how effectively the bot followed up. in practice, a difficulty with successfully using this strategy remains that it is harder to produce high-quality bot follow-up turns after the user has taken initiative. in general, users appear to share more information in response to our strategies (section 7.5), which seems likely to reflect a better experience than the brief, passive responses given previously. due to user privacy concerns only alexa prize team members could label the data in that study. while the relatively small size is indeed a limitation, we believe the qualitative conclusions to be generalizable. more generally, prior work @xcite suggests that humans expect chat bots to behave like humans. despite lacking direct empirical evidence for increase in user satisfaction, we believe that more human-like turn taking will likely be satisfying to users. another limiting factor to our experiments is that we programmed the bot to back-channel or to omit questions at random. we expect that user preferences for initiative would vary across both individual users and particular topics and that our randomized method was much less natural than one that accounted for context. both of these factors are likely to have inhibited our effect size. additionally, as noted in section 3, we are using a turn-based dialog system and therefore back-channeling cannot be done while the user speaks, but can only be attempted as a turn after they pause. this limits both its usefulness and realisticness as a strategy. still, the fact that these methods were effective even when timing was chosen at random suggests the strength of their potential for future contextdependent approaches. all of our strategies were tested independently of each other, and we leave it for future work to test their effects in combination. the question-answer design paradigm is common in open domain chatbot conversations, since it is an easy pattern to engineer. however, it has significant drawbacks. it restricts users' agency, potentially forcing them to discuss topics they aren't interested in. requiring users to answer questions on every turn can also cause fatigue. in our data, we found that some users would explicitly criticize this behavior, with utterances such as "you ask too many questions." without mixed-initiative, the bot and user cannot converse as equals. closing the initiative gap is therefore essential to a truly natural socialbot conversation. ## conclusion we found that it is possible to increase user initiative, as measured by utterance length, number of noun phrases, and response diversity, by giving linguistic cues that the user should steer the conversation. asking fewer questions produced longer responses with more noun phrases, as did back-channeling 33% of the time. when the bot gave statements, personal ones evoked more engagement than general ones. natural, human-like dialogue agents must share initiative with the user, and incorporating these strategies is an important step towards that goal.