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Shall We Talk? Conversing with Humans and Robots Naomi S. Baron Professor of Linguistics, Department of World Languages and Cultures Executive Director, Center for Teaching, Research, and Learning American University Mailing Address for Sending Proofs Professor Naomi S. Baron Hurst 214 American University 4400 Massachusetts Avenue, NW Washington, DC 20016 USA [email protected] Abstract While social robots are the creation of human beings, it is not obvious what kinds of conversation people desire to have with computer-­‐based devices. Progressive improvements in speech recognition, natural language parsing, and physical embodiment are making it technologically possible for social robots to engage with humans in essentially the full range of conversational modes that we do with one another. However, when we examine the variety of possible (human) linguistic functions, we discover reasons people may not wish total verisimilitude when interacting linguistically with robots. Informational and empathetic functions are likely to be more welcomed than those involving social control or critique. Running Title Shall We Talk? Key Words: conversation, humans, language, social robots, speech, writing To appear in The Information Society. This paper was originally presented at the COST Workshop “Social Robotics and Its Sustainability”, June 10-­‐13, 2013, Brussels, Belgium. The author is grateful to the conference organizers, especially Giuseppe Lugano and Leopoldina Fortunati, for the opportunity to develop her thinking about robots and language. She also thanks the anonymous reviewers for their insightful comments. 2 What do you say to a robot? Obviously, the answer is limited by the robot’s capacity to process human language. But it also depends upon what you want to talk about, what kind of response you are seeking, and what we mean by “language”. When people talk about language, they generally have in mind a spoken system of communication, commonly for the purpose of exchanging information. Language has long been assumed to be an ability unique to humans. After all, our species has been called homo loquens. Whether or not our primate cousins are biologically capable of what we might be willing to call “language” remains up for debate (e.g., Savage-­‐Rumbaugh, Shanker, and Taylor 1998; Sebeok 1986). Yet even those rejecting a sharp Cartesian divide between human and non-­‐human communication systems (Chomsky 1966) do not look for chimpanzees or gorillas to be composing sonnets or running customer complaint desks. What about robots? While the computer programs behind them may not generate Shakespeare, they are already writing fiction (Akst 2004). As for customer service, though many users find it distasteful when the entity answering their phone call is not a human but rather a voice recognition program (backed by a knowledge base), such programs’ responses are becoming increasingly accurate. Other problems remain. For example, a number of researchers (e.g., Brahnam and De Angeli 2012; Nass, Moon, and Green 1997) have addressed the issue of gender stereotyping in the realm of conversational service robots – and even Apple’s Siri now has a male voice counterpart (Bosker 2013). However, it is also true that human-­‐to-­‐human conversations may display such stereotyping. The goal of this paper is to consider the kind of communication we might (and might not) want to have with social robots. Specifically, we ask whether we want to construct social robots that have the full range of linguistic abilities that characterize human 3 language. To address that question, we need first to identify essential defining parameters of human language (and language use) against which human-­‐robot communication can then be meaningfully compared. These parameters include linguistic medium (speech, writing, or sign – Baron 1981), use of monologue or dialogue, conventions governing dialogue, knowledge of the linguistic abilities of our interlocutor, and the range of functions language may serve. Medium Since the late nineteenth century, linguistic discussions of “language” have overwhelmingly alluded only to speech, not writing. As the early twentieth-­‐century linguist Leonard Bloomfield proclaimed, writing is not itself language but “merely a way of recording language by means of visible marks” (Bloomfield 1933: 21). In recent decades, writing has received a more welcoming reception, evidenced by the growing number of publications on written language (such as works by Harris, e.g., 2000, and Coulmas, e.g., 2013, and appearance of the journal Writing Systems Research). Recognition of mature signing systems as languages is equally recent (e.g., Armstrong, Stokoe, and Wilcox 1995; Klima and Bellugi 1979). Monologue or Dialogue The second parameter is whether the language is monologue or dialogue (here meaning conversation between two or more participants). Noam Chomsky’s early model of transformation grammar (1965), which dominated the study of language for much of the second half of the twentieth century, was designed to account for the linguistic knowledge 4 of an idealized speaker-­‐hearer, leaving it to other scholars to analyze how conversation between actual people works. Research on discourse between individuals has received significant attention from sociologists and anthropologists, as well as from linguists more concerned with social interaction than with abstract language models (e.g., Sacks, Schegloff, and Jefferson 1974; Schiffrin, Tannen, and Hamilton 2001). Conventions Governing Dialogue It was a philosopher of language, H. Paul Grice, who articulated the set of principles that are now commonly used to define the basic rules of interpersonal conversation. Grice (1975) suggested that in structuring conversations, people are guided by an implicit set of four maxims: §
maxim of quantity: try to be as informative as possible, giving as much information as is needed but no more §
maxim of quality: §
try to be truthful, not giving information that is false or unsupported by evidence maxim of relation: try to be relevant, saying what is pertinent to the discussion maxim of manner: try to be clear, brief, and orderly, avoiding obscurity and ambiguity However, it is important to acknowledge that in real-­‐life linguistic exchange, speakers do not always follows these maxims. We withhold information from one another. We make assertions based on no little or no evidence. We often change the topic midstream in a conversation. And sometimes we are longwinded, as well as intentionally obscure. 5 What is more, we refuse to answer questions and commonly pay little attention to what our interlocutor is saying. Such violations of Grice’s maxims (and their correlates) should hardly be surprising. Part of what it means to be human (and to use a human language) is knowing how to manipulate not only the truth but our interlocutor. (For more discussion of Grice’s maxims, see, for example, Davis 1998; Levinson 1983; Martinich 1980). Linguistic Abilities of Our Interlocutor Another aspect of human language use is knowing how to adapt conversation to match our assumptions about the linguistic abilities of our conversational partner. Even five-­‐year-­‐old children understand that babies have different linguistic capacities than do their mothers or fathers (Andersen 1987). Similarly, linguists describe a speech register they call “foreigner talk” (Ferguson 1975), used for addressing people who lack proficient command of the language we are speaking. Foreigner talk has much in common with “baby talk”, also known as “motherese” or “child-­‐directed speech” – the language style adult speakers in most of the world use in addressing young children (Ferguson 1964). These adaptations tend to involve such features as higher than normal pitch levels, simplification of vocabulary and syntax, slower than normal speed, and frequent repetition of what is said (Snow and Ferguson 1977). While such accommodations potentially help facilitate more successful communication between the two parties, the stronger motivation is often empathetic connection with another living being. Interestingly, a number of these features also appear in the language some use in addressing pets (Hirsh-­‐Pasek and Treiman 1982). 6 Functions of Language The linguist Roman Jakobson (1960) postulated that language has six essential functions. His list included §
the Referential Function (descriptive statements referring to the real or imagined world) §
the Expressive Function (revealing information about the speaker’s internal state) §
the Conative Function (getting your interlocutor’s attention through vocatives or imperatives) §
the Poetic Function (language for its own sake, as in poetry) §
the Phatic Function (language used to connect with other people, where the content is less important than the social linkage) §
the Metalingual Function (language used to talk about language) Without claiming to be comprehensive, we can capture many of the essential motivations behind most conversations by focusing on three issues: pragmatics (similar to Jakobson’s Referential Function), social companionship (akin to Jakobson’s Phatic Function), and control (something of a combination of Jakobson’s Expressive and Conative Functions). If you ask a class of undergraduate students what the function of language is, overwhelming they will respond it is to communicate information to other people (by implication, in a conversation). While Jakobson’s scheme argues there is more to language than information, it is true that the most pragmatic function of a conversation is to get practical work done: I give you information (“The keys are by the door.”) or request information from you (“Where are the keys?”). 7 But humans are not simply practical beings. They are also social creatures. A significant amount of the time we spend talking with one another has little to do with providing information. Rather, talk is commonly an overlay atop social interaction (Bruner 1975), as well as a tool that enables it. We find things to say in order to keep the communication channel open. A conversational opener such as “What’s up?” is more an invitation to talk than a request to talk about something specific. Studies of both spoken and written language (including electronically-­‐mediated communication such as email, instant messaging, and text messaging), across a number of cultures, suggest that females are typically heavier users of the social function of conversation than males (Baron and Campbell 2012), though the mix of pragmatic and social communication for both genders is obviously context-­‐specific. The third conversational function (which often is coterminous with one of the first two) is control. By “control” we mean maneuvering the course of the conversation (Baron 2008: Chapter 3). Such maneuvering can take many forms. In voice conversations, control might be expressed by raising your voice, not giving your interlocutor a chance to finish, hanging up the phone, or placing interlocutors on speaker phone (unbeknownst to them). With digital communication media, the range of options for control increases. You might choose not to answer a call on your mobile phone after checking caller ID, de-­‐friend someone from your Facebook account, or decide to text rather than talk on your mobile to keep the conversation short – meaning you can cut out the usual social niceties that voice-­‐
to-­‐voice conversations typically entail (Baron and Hård af Segerstad 2010). Linguist John L. Locke (1998) expressed concern that technology more generally encourages depersonalization in communication. Writing in the late 1990s, his culprits included 8 television and the telephone. However, he was particularly concerned about email (whose non-­‐face-­‐to-­‐face character now extends to texting and Twitter). As with any kind of electronically-­‐mediated communication, email (while useful for both pragmatic and social conversational functions) also offers both sender and receiver considerable options for control – from spamming, to not responding, to avoiding face-­‐to-­‐face speech in favor of written communication. Interestingly, it was the desire to avoid face-­‐to-­‐face conversation that led, in part, to Jack Dorsey’s invention of Twitter. As a child, Dorsey was shy, as well as having a speech impediment. To this day, he still finds it easier to communicate through letters, texts, or tweets than through face-­‐to-­‐face conversation. As he explained on a CBS 60 Minutes program, “Do I feel like I'm an expert in having a normal conversation face-­‐to-­‐face? Absolutely not. That's just not my natural state.” (Logan 2013) Language, Humans, and Social Robots We have been looking thus far at a collection of variables that define how humans use language with one another, paradigmatically in face-­‐to-­‐face spoken contexts, but also introducing alternative media (such as writing) and conversations between individuals with differential levels of linguistic skill. Our next question is what happens when you add digital communication into the mix. We start by exploring how people have transformed their communication (and communication expectations) with the coming of computers and their electronic offspring. As we will discover, these experiences have, in turn, helped shape our expectations for communicating with virtual or embodied language programs in the form of social robots. 9 The Digital Turn: Between Humans What kinds of linguistic outcomes do we want from our digitally-­‐mediated conversations with one another? Our desires have evolved along with the technology. Electronically-­‐mediated communication began in the 1970s, with use of computer conferencing, bulletin boards, newsgroups, listservs, and early instant messaging among members of the computer science research community and among hobbyists. In the early 1990s, text messaging (on mobile phones) became available in Europe through GSM’s Short Message Service (SMS), spreading by the end of the century to large swaths of the world. Meanwhile, by the late 1990s, instant messaging (on computers) had largely become the medium of choice among young people in America for electronically-­‐mediated communication. (Email was progressively left for adults.) The 2000s saw the birth of social networking platforms such as Facebook and Twitter, and rapid growth of texting in the US. The early years of online conversation were filled with violations of Gricean-­‐style conversational maxims. Flaming (involving rudeness and use of profanity) typified many exchanges. Other characteristics included humor and irony, which often led to misunderstandings. It was one such misunderstanding in 1982 involving an online bulletin board at Carnegie Mellon University that led Scott Fahlman to invent the smiley and the frowney so users could indicate that the preceding message should not be taken literally (Baron 2009). With the explosive growth of mobile phones – nearly 6.8 billion subscriptions as of 2013 (International Telecommunication Union 2014), in a world of roughly seven billion people – there has been a marked trend away from voice-­‐to-­‐voice conversation (be it face-­‐
10 to-­‐face, on the telephone, or even voice mail) to text-­‐based conversation. An important tipping point was reached in the United States in 2008, when for the first time people averaged more text messaging on their mobile phones than voice calls (Leggatt 2008). With texting, gone are many of the conversational clues (including facial expression, body posture, and voice quality) that humans have traditionally used when face-­‐to-­‐face with one another for judging how to proceed in a conversation: knowing when it is your turn to speak, sensing if your interlocutor is actually listening to you, judging whether you are hurting the other person’s feelings. As Fahlman suggested more than thirty years ago – and as netiquette gurus keep reminding the rest of us – the possibilities for miscommunication in a written medium are legion. Do we have less successful conversations when writing online than when speaking (face-­‐to-­‐face or otherwise)? Collecting real-­‐life (as opposed to laboratory) data is challenging. However, research from the early 2000s indicates that regardless of the amount of time teenagers and young adults spend in online communication, they profess (on average) to preferring face-­‐to-­‐face conversation (Baym, Zhang, and Lin 2004). The Digital Turn: Between Humans and Robots It was Alan Turing whose work in 1950 underlies much of our interest in creating machines that can communicate with humans. Turing asked whether a computer program could be written that convinced people the language it generated in a conversation actually came from another human, not a machine (Turing 1950). The so-­‐called Turing Test is now the subject of the yearly Loebner Prize competition in which participants vie to convince judges that the language generated by their program is produced by a human. In a separate 11 competition, real people attempt to prove they are people, not computers (Christian 2011). In both instances, the contestants (human and machine) engage in conversation of an everyday nature. Jerome Weizenbaum (1966) approached the question of human-­‐machine conversation from a different tack. His ELIZA program (built to emulate a Rogerian psychotherapist) shifted the discourse balance. The computer (ELIZA) took the conversational upper hand, asking probing questions whose answers (provided by the human user) were intended to help flesh-­‐and-­‐blood interlocutors better understand themselves. Since the days of ELIZA, artificial intelligence researchers have made remarkable progress in developing natural language processing tools that “understand” both written and spoken language. While the parsing (and search) patterns used by machines are not the same as those employed by humans, the computers’ performance is impressive. Examples range from Google Translate to IBM’s Watson (Baker 2011) to Apple’s Siri. Given the seeming ubiquity of speech recognition programs in the lives of ordinary people, the success rate (along with functional limitations) of these programs is of growing interest to the general public. Note, however, that all of these programs-­‐come-­‐robots are engaging in a different conversational model than was ELIZA. While ELIZA predominantly asked questions, these systems are designed to give answers. There is dialogue with the likes of Siri, but the topics tend to be circumscribed: Where’s the nearest Starbucks? or What is the weather like in Katmandu? Similarly, limitations apply to earlier forms of interactive media devices that respond to text or voice input. Examples that come to mind include children’s “talking” 12 dolls that have simple speech recognition programs built into them or automobile navigation systems. (For more on human-­‐machine interaction, see Deuze 2012.) A helpful yardstick for measuring the progress of the linguistic abilities of robots (again, meaning the language programs behind them) is to compare over time how much human users have needed to accommodate to their machine interlocutors. In the early days of natural language processing research, it was generally easy to expose the shortcomings of a written-­‐input system by using complex vocabulary or syntax. Similarly, as many telephone users have discovered, non-­‐standard accents can easily confound a computer-­‐
based answering program of the sort used for directory assistance or airline bookings. Recall that in human conversations, speakers commonly adapt the pitch, vocabulary, and grammar of their language to suit the perceived abilities of their interlocutor (for example, an adult using child-­‐directed speech to a one-­‐year-­‐old child, or a native of Brazil using foreigner talk with a Russian-­‐speaking visitor to Rio de Janeiro who has limited Portuguese skills). The extent to which we are willing to be adaptable in conversing with robots will likely depend less on human empathy than on the practical desire to get a robot to come up with the information we are seeking or to perform a task we want done. Research is beginning to appear on human expectations when talking with a robot. In a study comparing the way young adults converse with a robot as opposed to another person, Kriz, Anderson, and Trafton (2010) analyzed how subjects directed the robot or human interlocutor to complete a physical navigation task. (Unbeknownst to the subjects, the robot’s movements were actually controlled by a hidden human confederate.) The subjects’ robot-­‐directed speech mirrored child-­‐directed speech or foreigner talk in many ways: louder volume (particularly common in foreigner talk), higher pitch, and hyper-­‐
13 articulation (compared with how the same subjects spoke with another person). Interestingly, when addressing the robot, subjects did not lower their expectations regarding the concepts they assumed the robot could understand. In sum, while subjects “did not expect the robot to have superior speech recognition, they did expect it to have cognitive capacities that are on par with human cognition” (p. 273). What about the paralinguistic aspects of a conversation such as facial expressions, body posture, or expression of emotion? In the case of written human-­‐to-­‐human conversations conducted over digital media, we have already seen that these cues are largely unavailable (emoticons notwithstanding). The same is obviously true of text-­‐based human-­‐robot interaction. When voice is added (as with Siri), we introduce opportunities for such expressions as warmth or sarcasm (comparable to human-­‐to-­‐human phone conversations). Physical robots (that is, computer programs with bodies) are now being designed to emulate an increasing number of human traits. Fascinating work has been done in creating avatars (physical or onscreen) with changing facial expressions. There are now avatars that can mirror the facial expression of the user (Bulletin of Keio University Faculty of Science and Technology 2012). There are also robots that can recognize human facial expressions and interact with the humans accordingly (Tang 2007). The addition of facial expressions might lead us to speak to such robots with more conversational sincerity than we would to a robot that has no face. (For more on issues involving emotions and human-­‐robot interchange, see, for example, Breazeal 2003; Dautenhahn 2013; Nishio et al. 2012; Reichenbach, Bartneck, and Carpenter 2008; and the University of Pisa FACE Program -­‐-­‐ 14 The Function Question: Pragmatics, Social Companionship, Control We have discussed the variety of functions that language might potentially serve in human conversation, along with the fact that people often violate the “rules” of conversation, as laid out by Grice. Discourse analysis has taught us a good deal about the ways humans actually utilize these functions to follow (or violate) conversational maxims (see, for example, Stubbs 1983; Widdowson 2007). It goes without saying that people are not always pleased with the conversations they have with one another. Sometime we feel insulted, slighted, or demeaned. We may like our interlocutor but find that when talking, it is difficult to get in a conversational turn. Particularly in face-­‐to-­‐face encounters, we often have little choice but to tolerate our interlocutor’s linguistic behavior. Do humans wish to encounter these kinds of conversational challenges when conversing with robots? We can examine this question with respect to the three functions of language that we earlier identified: pragmatics, social companionship, and control. In each case, we will explore how humans might want robots to fill those functions, including where we might wish to limit such functionally. Pragmatics My first conversational encounter with an embodied social robot was in 2005 at the Aichi Prefecture World Expo in Japan. Smartly dressed like an airline stewardess of an earlier era, a reception robot politely inquired of visitors where they wished to go and then directed them (in Japanese, Chinese, Korean, or English – depending upon the language in 15 which the question was asked) to the appropriate location in the fairgrounds (Kokoro and Advanced Media 2005). Here was an example par excellence of a pragmatic conversation with a robot: I asked questions; “she” answered. We might think of this pragmatic function of robots as the social equivalent of industrial robots: They are there to get work done. IBM’s Watson – which bested champion human competitors on the television show “Jeopardy” in 2011 – also belongs in this category. We need to be careful about using the term “conversation” to describe exchanges between the Expo visitors and the receptionist robot (or between “Jeopardy” host Alex Trebek and Watson). In essence, the exchanges were more often one-­‐off volleys (question-­‐
answer, question-­‐answer). To be fair, so are many of the pragmatic conversations between people (“Where are the car keys?” “By the front door.” Done.) Apple’s Siri is programmed to be a bit more loquacious, but her (or now “his or her”) purpose is still, at bottom, pragmatic. This pragmatism has consequences with regard to Grice’s conversational maxims. While we are willing to countenance the fact that humans withhold information, do not always tell the truth, sometimes go off-­‐topic, and can be wordy or obscure, it is hard to imagine anyone designing a robot with such less-­‐than-­‐felicitous conversational behavior. We would never be sure we could trust what the robot said to us (rendering it pragmatically unreliable). We would also not be willing to extend it the courtesy of listening to its ramblings or to request repeated clarification. Rather, we would likely send it back to the programmer for a more efficient model. 16 Social Companionship What about using robots for social companionship? Here we tend to gravitate to a particular sort of robot, namely pets. At that same 2005 World Expo, I had my first encounter with Paro, a robotic baby harp seal. It was love at first sight – and touch. But Paro, like flesh-­‐and-­‐blood pets, does not engage in conversation. In fact, it does not talk at all – and that is one reason it is so attractive. Barks and meows aside, living pets are also creatures that do not have conversational skills. Pets respond to human owners with affection (a critical component of social companionship), but it is their human owners who do all the real talking. With robotic pets, the humans are in control. When we tire of a mechanical friend, we simply turn it off. (As any owner of a flesh-­‐and-­‐blood animal knows, live pets are harder to control.) Can we look forward to robots that provide social conversation? Isaac Asimov imaged such robots (think of Robbie) in I, Robot, half a century ago (Asimov 1950). More recently, in the 2012 movie Robot &Frank, a Robbie-­‐like robot appears on the scene when Frank’s grown children buy their father a human surrogate as an alternative to placing Frank in a nursing home. (Frank suffers from a form of dementia and cannot reliably care for himself.) The robot reminds Frank when it is time to eat, to take his medicines, and so forth – and serves more generally as a social companion. Frank can be stubborn, not wanting a machine running his life. But in this case, Frank has no choice. Given their aging population (and low birth rate), the Japanese have been working for years to develop robots that can tend to the elderly. Such robots, like Frank’s, presumably do not take “no” for an answer. As rates (and sheer numbers) of older people with dementia rise around the world (Alzheimer’s Disease International 2008), we can 17 easily imagine the utility of social robots that not only offer information but also provide companionship (and control) using ordinary language. Would the rest of us relish having such companions? When a person tells us it is time to eat or take medicine, we generally have the choice whether or not to comply. We can ignore the command, shout “Leave me alone!”, or perhaps leave the room. If the communication is written, we can decide not to respond or even block further messages from the individual intruding on our personal space. As social beings, we learn how to navigate our way around those whose expectations of us we do not wish to fulfill. It is unclear we would welcome robots that refuse to leave us in peace. Control In thinking about the kind of conversation we might have with robots, clearly control is a key issue. If it is information we are seeking from the robot’s database, we want it now – and without conversationally-­‐unwelcome responses from the robot. Unless we are looking for minder robots for those who cannot be trusted to look after themselves (such as young children or older people with dementia), we are unlikely to desire robots that are programmed to converse with us in ways equivalent to the control level humans wish to have when conversing with robots. Should Human Conversation be a Goal in Designing Social Robots? Thus far, no computer program has managed to successfully fool human judges at the annual Loebner Prize competition. The question, though, is whether we actually want an AI program (to install in a physical robot) that will enable a robot to have realistic human 18 conversation with us. Another way of asking the question is, How much authenticity do we want from robots? That is, how much do we want their linguistic abilities – and functions – to be the same as found in mature human language users? Elsewhere (Baron 2013) I have argued that authenticity (in the sense of not being able to distinguish between an original and a replica) is often not a human goal. We are generally content listening to music on an electronic device rather than always insisting upon live performances. We enjoy Disney World, even though the Norwegian Pavilion is hardly confusable with what we would find in Norway. Artistic genres such as impressionism or cubism would not exist unless some artists (and eventually patrons) had reached beyond realistic representation. It is also not obvious we want authenticity in our robots. Paro, the harp-­‐seal robot, would not be a successful companion for the elderly if it needed to live in water, eating small fish and crustaceans. How much authenticity do we want in our conversations with robots? 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