CCTP 607 Final Project
In many technology communities, 2016 was known as the year of the chatbot. Facebook released an API that made making branded chatbots extraordinarily simple for brands large and small alike (Constine, Josh. 2016). Microsoft released then quickly silenced their chatbot, Tay, after it learned white supremacist rhetoric from Twitter users (Victor, Daniel 2016). For better or for worse, brands now have a new direct communication channel to manage customer service, target marketing and facilitate sales. Both customers and businesses like chatbots for one main reason: chatbots makes customer service less arduous. Customers have 24-hour access to brand representatives that they don’t have to call, and businesses have a simple solution for customer engagement that can be designed to meet the business’ standards. This paper will analyze the history of chatbots, the technology that drives chatbots and how the design of chatbots impacts the brand equity of those who use them.
Since the launch of Facebook Messenger chatbots in 2016, companies have quickly taken advantage of chatbots as a new communication channel to customers. Chatbots are interactive digital agents which provide real time conversational interfaces for organizations. There are currently over 30,000 chatbots active on Facebook messenger and it is expected 80% of customer engagement will be done through chatbots by 2020. Utilizing chatbots is a way for business to provide consistent and reliable customer service and has already proven to be successful by companies like 1800 Flowers and Tommy Hilfiger which have experienced monetary returns on chatbot investments. This new communication channel has also benefitted customers by providing 24-hour access to answers from brands; 77% of customers who have interacted with a business’s chatbot have reported improved perception of that business (c Wertz, Jia 2016). Chatbots are simple solutions to many customer service complaints, but come with their own technical and security challenges. Through an analysis of the design of chatbots and the history of the technology, I will assess how the implementation of chatbots can impact an organization’s brand equity using the Customer Based Brand Equity ( CBBE) framework.Usually, this framework is used to help an organization analyze their current brand and build a stronger, customer focused one step by step. In this paper, however, I will use the framework to show how four brands have utilized chatbots to impact a stage of the CBBE framework.
What are Chatbots?
Chatbots are known by many different names including chatterbots, interactive agents, conversational AI or artificial spy entities. Despite the multitude of names, all chatbots effectively perform the same task: conduct conversations using natural language by following designed protocols. Most modern day chatbots are designed with AI to accurately process and respond to human inputs, whether the input is vocal or textual. Other chatbots, especially the earlier ones, are designed to simply follow a set of rules that produce replies from a designed script. This paper will focus on the chatbots that are designed with AI and operate on chat websites and messaging applications, however, the moniker “chatbot” can be applied to virtual assistants and automated voice chatbots (Wikepedia.com).
A Brief History of Chatbots
Although chatbots seems like a relatively new phenomenon, chatbot applications have been around since the early 1960’s. ELIZA, designed in 1964, was a computer program developed by Joseph Weizenbaum in the MIT Artificial Intelligence Laboratory. Running on a script called DOCTOR, ELIZA was designed to mimic the retorts of a Rogerian psychotherapist to a client on his or her first visit. ELIZA was initially designed to reveal the superficial nature of human and computer interactions, but through usage unveiled emotional attachment developed by ELIZA users( (Weizenbaum, Joseph. 1966).
The emotional attachments formed primarily because of the responses from ELIZA enabled by the DOCTOR script. The DOCTOR script processed the inputs from users through simple characterization and substitution of terms to deliver predesigned templates and encoded phrases, which were created to parody a Rogerian therapist’s penchant for answering questions with more questions. By designing ELIZA with an if-else protocol, Weizenbaum was able to avoid designing a complex natural language processing system (Weizenbaum, Joseph. 1966). The difference between ELIZA and the 30,000 chatbots on Facebook Messenger is that chatbots used today are designed and trained with the knowledge of language.
Another notable bot is called Alice (Artificial Linguistic Internet Computer Entity) or Alicebot, it was inspired by ELIZA and was created by Richard Wallace 1995 using Java. Though Alice is a complex and award winning chatbot, it still cannot not pass a Turing test (Wikipedia.com). A turing test is a test of a machine’s intelligence and whether or not a machine can pass as a human.
The first chatbot to pass a Turing test was Eugene Goostman in 2014. The Russian chatbot was designed to communicate like a 13-year-old Ukrainian whose first language was not English. (Gonzalez, Robert T. 2015). The validity of this milestone has been fervently questioned, this debate is fueled not by the advanced technological nature of Eugene Goostman, but the believability of Eugene Goostman’s personal history. Passing the Turing test brings forth questions about how users perceive a person or bot to converse and the accuracy of these perceptions. Other notable chatbots include Clippy, the loathed bot that peppered the margins of Microsoft Word from 1997 to 2003.
Eugene Goostman like ELIZA was designed with a specific backstory that fostered trust in the some of the humans that the bot interacted with. ELIZA was not said to be more than a computer program, yet the responses designed for ELIZA still evoked a one sided relationship from humans. These two bots are prime examples of how brands can overcome subpar bots with realistic “personalities”. The personalities designed into the branded bots provide users with trust and familiarity which may, inadvertently, deceive humans and engender trust. Depending on the brand’s identity, the branded bots should utilize colloquialisms or vernacular to convey the bot’s persona and align it with the brand. Successfully designing a bot with a personality requires training the bot to have a knowledge of language; this can be done through AI, specifically natural language processing and natural language understanding.
How Chatbots Work
Chatbots are designed like applications with multiple layers of functionality including the presentation layer, machine learning layer and the data layer. Natural Language Processing, Natural Language Understanding and Natural Language Generation are designed to facilitate accurate responses to queries by sending data through the layers of the chatbot (Figure 1). Natural Language Processing (NLP) is the overarching system of neural networks that facilitate end to end communication between humans and machines, in the human’s preferred language. Essentially, NLP provides the machine with the knowledge of language that is used by human interlocutors. (Chatbots Magazine 2018). Chatbots designed with NLP converts a user’s message to structured data, so that a relevant answer can be produced. Natural Language Understanding (NLU) is designed to manage the unstructured and flexible nature of human language. Designing NLU for chatbots requires a combination of rules and statistical models to create a methodology for handling unknown or unrecognizable inputs (Lola.com 2016) At its core, NLU gives chatbots the ability to accurately process and respond to colloquialisms and quirks of human language. Natural Language Generation (NLG) creates the message for the chatbot to answer to the original query.
Figure 1: Fernandes, Anush. “NLP, NLU, NLG and How Chatbots Work.” Chatbots Life, Chatbots Life, 15 Nov. 2017, chatbotslife.com/nlp-nlu-nlg-and-how-chatbots-work-dd7861dfc9df.
Popularity of Chatbots
Over the last five years, the interest in chatbots has increased exponent ally (Figure 2). This interest is due to several factors, including the simplicity with which chatbots can be integrated into mobile devices; they share a core feature: messaging. Of the 7.3 billion people in the global population, 6.1 billion people use an SMS capable mobile device and 2.1 billion use messaging applications. Facebook Messenger has 1 billion users, solidifying chatbots as an easily integrated and not disruptive mobile technology (Wertz, Jia. 2018).
Another reason for the wide spread interest and adoption for chatbots is that they are designed to be used by people of all age groups. This makes the technology less prohibitive than other more complex technologies. Chatbots also provide customers with opportunities to ask questions, they otherwise would be embarrassed to ask (Brandtzaeg P.2017) Overall, chatbots are effective because they are simple to engage with and relatively easy to design.
William Miesel, a renowned technologist in the chatbot world, has predicted that the global revenues from chatbots will soon amass to $623 billion (Dale, Robert.2016). In the global market, 45% of end users prefer to engage with chatbots for customer service inquires. Results from a survey conducted by Live Person ,with 5,000 respondents, showed that the majority of users are indifferent to chatbots, as long as the user’s problem is resolved following a conversation with one. The second largest group of respondents (33%) felt positively about chatbots. (Nguyen, Mai-Hanh. 2017). Automating customer service through chatbots allows business to gain consistency and speed, which is often lacking in human customer service. Studies have shown that the majority of customers have improved perceptions of a brand after conversing with branded chatbots (Wertz, Jia. 2018). Gartner predicts that by 2020, 85% of customer engagement will be done through non-human entities (Moore, Susan.2018)
Businesses benefit from chatbots by cutting costs and gaining knowledge about consumer behavior. In a survey, conducted by Oracle in 2016, 80% of respondents said that they have used or plan to use chatbots by 2020. The increased integration of chatbots into businesses follows the trend of increased automation, chatbots will soon be used across businesses in marketing, sales and customer service. Though complete automation through chatbots is not feasible or even holistically beneficial for an organization, chatbots designed for customer service are predicted to replace 29% of customer service jobs. (Business Insider 2016)
Figure 2: Raj-Building Chatbots with Python-Using Natural Language Processing and Machine Learning (2019).pdf
Chatbots and Data Security
Chatbots, like all technology, have risks for users, especially as it relates to data privacy. Personalized chatbots, in particular, need to be designed with safeguards for data. Without proper security measures in place, both businesses and users can suffer. Chatbots rely on HTTP and other communication protocols, as well as, SQL queries for data retrieval which can often be targeted for hacks (Bozic J. 2018). Most concerns about data privacy in the chatbot world are focused on financial services chatbots. However, most financial services already transfer user data from databases via HTTPS protocols. (Chatbots Magazine. 2017)
There are two methods which are normally designed into chatbots to ensure security: authentication and authorization. Authentication verifies the human’s identity and authorization gets the user’s permission to complete a task. The technology used to develop chatbots is not new technology, which means that there are existing security measures that have been designed to combat security threats. However, it is important to remember that data security is the onus of the developers as well as the platforms that the bots run on (Chatbots Magazine. 2017).
Brand Equity and Branded Chatbots
According to Keller’s Customer Based Brand Equity model (CBBE) (Figure 3), brand equity is composed of four major parts including the identity, meaning, response and relationships. Brand equity, in this context, represents the valuation of a brand in the eyes of the customer. High brand equity represents strong customer loyalty and can protect a company from volatility in the market (Keller, Kevin Lane).
Figure 3: “Keller’s Brand Equity Model Building a Powerful Brand.” Strategy Tools from MindTools.com, www.mindtools.com/pages/article/keller-brand-equity-model.htm.
Building brand salience is integral to defining brand identity and engendering brand awareness. A brand’s identity is composed of more than just the logo and name, several other decisions about the appearance of the brand, including packaging, font, and color scheme of the brand impact its identity (Forbes.2017). Brand awareness is more than a customer’s recognition of a brand’s name and logo, it is also the connection between a brand’s product and the needs that it will fulfill for a customer. Essentially, brand salience is what a company wants consumers to think about the brand when the consumer needs a product in the brand’s category. Well-designed brand salience helps companies stand out in their industries and is integral to brand equity. (Keller, Kevin Lane).
The floral industry is struggling to maintain profitable and industry tactics aimed at averting heightened preferences for succulents have birthed several flower delivery startups. As the industry wavers, brands like 1800 Flowers are attempting to maintain their salience by staying ahead of the technological curve of e-commerce (Kelleher, Katy. 2018) 1800 Flowers launched one of the first chatbots on Facebook Messenger in 2016. Its success was present nominally and practically with Mark Zuckerberg demoing the 1800 Flowers bot at the F8 conference and the new customers and sales gained from the bot. Not long after launch, President of 1800 Flowers, Chris McCann lauded the bot for its facilitation of 70% of sales and its acquisition of a younger market (Caffyn, Grace, et al. 2016). Historically, 1800 Flowers has had a relatively high percentage of millennial customers, 29% as of 2014. This is due to the organization’s commitment through development of brand salience, marketing and strategic partnerships to keep this younger market purchasing items that millennials characteristically do not (Stambor, Zak. 2017). Consistency is integral to salient brands because it helps to keep the brand’s place in the market stable over time, but the ability to change is also vital to keeping the brand relevant.
Meaning is how a brand communicates with its customers and conveys the ethical values of the brand to the customers. This is one clear area where a well-designed chatbot can effectively impact a brand’s equity. Meaning is comprised of two components, imagery and performance. Imagery is how a brand satisfies a customer’s social and psychological expectations of the customer, this can be done through digital targeted marketing or physical engagement with a customer in store. Performance is how well a brand meets a customer’s needs (mindtools.com).
Despite the growth of online sales, customers still spend more on in store purchases than online ones. Chatbots not only provide opportunities to market and provide customer service, but can also be used to drive customers back into the store. Regardless of the type of business, companies need to strike a balance between online presence and in store experiences to stay competitive. Tommy Hilfiger launched a chatbot on Facebook messenger called TMY.GRL to converse with customers about the Fall 2016 Tommy X Gigi Hadid collection (Figure 4). At its earliest launch, TMY.GRL was an informational chatbot which provided users with product suggestions and information. With further integration of e-commerce on Facebook Messenger, TMY.GRL is able to link product information with purchase opportunities. TMY.GRL provides solutions for both imagery and performance for Tommy Hilfiger by providing an entertaining commerce channel that aligns with the brand identity (Arthur, Rachel. 2016). TMY.GRL meets the expectations and needs of customers by providing speedy and relevant suggestions to customers looking for products. It also balances online presence with in store engagement by alerting customers of sales and news of events.
Figure 4: Arthur, Rachel. “Tommy Hilfiger Launches Chatbot On Facebook Messenger To Tie to Gigi Hadid Collection.” Forbes, Forbes Magazine, 12 Sept. 2016, www.forbes.com/sites/rachelarthur/2016/09/11/tommy-hilfiger-launches-chatbot-on-facebook-messenger-to-tie-to-gigi-hadid-collection/#3a5a42ab2238
Response is how consumers respond to engagement with the brand, these responses can be categorized in two segments: judgements and feelings. Brand judgements are the assumptions that customers make based on the performance and imagery of a brand. Judgements generally fit into four major categories: brand quality, brand credibility, brand consideration and brand superiority. Brand feelings are related the social currency of the brand that sometimes conjure emotional responses or reactions. The six brand feelings are warmth, fun, excitement, security, social approval and self-respect. Brand response encompasses the head and heart reactions that a customer experiences when interacting with a brand or makes a purchase from that brand (Keller, Kevin Lane).
Cleo is a financial services chatbot designed to replace individual banking apps. Users can get insights about spending habits and trends across multiple debit and credit accounts. Rather than becoming a bank, the founder of Cleo is committed to improving the user experience of financial services (O’Hear, Steve. 2018). Cleo is designed with a sassy, comedic personality which was designed to take the formality out of banking and make millennials more comfortable communicating with Cleo. This decision is clearly aimed at inducing warmth and fun from users, which are emotions typically left out of banking. Cleo also emphasizes the security of the business to give credibility to the bot. The personality of Cleo, however, has come under fire for being too informal and making inappropriate jokes (Sentance, Rebecca). Since Cleo bot is central to the Cleo start up, any negative reflection on the bot impacts the overall business. Though the informal nature of the bot is meant to make the brand relatable, it is clearly a risky business to design a bot, the business’ only tangible product, with a tongue in cheek “personality”. The judgements that result from that kind of personality can leave customers with positive or negative opinions which could be dangerous for the brand’s equity.
The last and arguably most important part of CBBE is the relationship between brands and their customers: brand resonance. Brand resonance represent the extent to which customers feel that they are in sync with the brand. It can be measured by repeat purchases and frequent engagement with the brand. The four categories of brand resonance are behavioral loyalty, attitudinal attachment, sense of community and active engagement (Keller, Kevin Lane).
Wysa, Woebot and Youper are three of the top therapy chatbots currently on the market. A study from 2014, discovered participants were more open with AI psychologist bots than with human bots. These findings are not particularly surprising, especially because of the historical experiences with ELIZA. The penchant for consumers to utilize bots for therapy, highlights the many prohibitive factors of human to human therapy: cost, time, and general lack of access. It also fosters attitudinal attachment to less expensive, yet still effective chatbots. Nevertheless, there are ambiguities about the safety of mentally ill patients who use bots for therapy rather than human therapists. (Mikael-Debass, Milena). Therapeutic chatbots have an advantage in brand equity over other kinds of chatbots because therapy is deeply relational. E-commerce, retail and banking chatbots replace the semi-anonymous customer service that consumers experience in-store, online and on the phone. Therapy, however, is done one on one or in groups where there are relationships formed between participants based on the expectation of trust and privacy. The nature of therapy also leads to behavioral loyalty which further connects a therapy bot to a human user. Despite the ethical and practical uncertainties about therapy bots, they stand to gain the strongest level of brand equity, regardless of design complexity.
Since the boom of chatbots in 2016, the numbers of chatbots online will continue to grow. Chatbots are a low cost and easy to develop technology to build, especially on Facebook Messenger. Advances in NLP and AI have made chatbots more adept at communicating with humans and have broadened the capabilities of chatbots. It is clear that bots can be built to build brand equity at every stage of the CBBE framework. It is also clear that for certain companies, bots can be profitable at the center of the business whereas in other companies, bots are supportive to a larger product offering. Inc.com lists the five industries with the most to gain from chatbots as hospitality, banking, retail, service businesses and publishing. These industries stand to benefit from chatbots in both the productivity of the organization and the level of service that the organization can provide. (Harrison, Kate L). However, chatbots in the healthcare sector are also forecasted to grow because they will be beneficial in those same arenas. Customer service in healthcare is complicated to automate because of the risks that privately held companies have when providing medical advice and services. For simple communication, chatbots have already proven to be useful for answering questions in the medical field. Therapy bots have found their place within the market and have acquired loyal customers, not unlike ELIZA, the “mother” of chatbots. It is surprising to realize that chatbots have come a long way since 1966, yet still operate the same and still bring forth emotional responses from users.For startup businesses, chatbots are a lean tool that can stand alone or be built upon for other more complex businesses. Regardless of the industry, designing chatbots should be done with the brand equity in mind to limit the negative impacts of a pert bot and heighten the brand’s salience in an ever-changing online marketplace. It is not unlikely that chatbots will one day be part of the foundations of a brand’s identity; the same thoughtfulness that goes into the other aspects of a brand’s identity need to be considered when designing a branded chatbot.
Arthur, Rachel. “Tommy Hilfiger Launches Chatbot On Facebook Messenger To Tie To Gigi Hadid Collection.” Forbes, Forbes Magazine, 12 Sept. 2016, www.forbes.com/sites/rachelarthur/2016/09/11/tommy-hilfiger-launches-chatbot-on-facebook-messenger-to-tie-to-gigi-hadid-collection/#3a5a42ab2238.
“Artificial Linguistic Internet Computer Entity.” Wikipedia, Wikimedia Foundation, 20 Apr. 2019, en.wikipedia.org/wiki/Artificial_Linguistic_Internet_Computer_Entity.
Bozic J., Wotawa F. (2018) Security Testing for Chatbots. In: Medina-Bulo I., Merayo M., Hierons R. (eds) Testing Software and Systems. ICTSS 2018. Lecture Notes in Computer Science, vol 11146. Springer, Cham
Brandtzaeg P.B., Følstad A. (2017) Why People Use Chatbots. In: Kompatsiaris I. et al. (eds) Internet Science. INSCI 2017. Lecture Notes in Computer Science, vol 10673. Springer, Cham
“Building Brand Equity.” Forbes, Forbes Magazine, 24 July 2017, www.forbes.com/sites/propointgraphics/2017/07/08/building-brand-equity/#df793e6e8f85.
Caffyn, Grace, et al. “Two Months in: How the 1-800 Flowers Facebook Bot Is Working Out.” Digiday, 29 June 2016, digiday.com/marketing/two-months-1-800-flowers-facebook-bot-working/.
“Chatbot.” Wikipedia, Wikimedia Foundation, 2 May 2019, en.wikipedia.org/wiki/Chatbot.
Constine, Josh. “Facebook Launches Messenger Platform with Chatbots – TechCrunch.” TechCrunch, TechCrunch, 12 Apr. 2016, techcrunch.com/2016/04/12/agents-on-messenger/.
Constine, Josh, and Sarah Perez. “Facebook Messenger Now Allows Payments in Its 30,000 Chat Bots – TechCrunch.” TechCrunch, TechCrunch, 12 Sept. 2016, techcrunch.com/2016/09/12/messenger-bot-payments/.
Dale, Robert. “Industry Watch The Return of the Chatbots.” Cambridge University Press, 10 Aug. 2016.
Gonzalez, Robert T., and George Dvorsky. “A Chatbot Has ‘Passed’ The Turing Test For The First Time.” io9, io9, 16 Dec. 2015, io9.gizmodo.com/a-chatbot-has-passed-the-turing-test-for-the-first-ti-1587834715.
Harrison, Kate L. “These 5 Industries Have the Most to Gain from Chatbots.” Inc.com, Inc., 9 Oct. 2017, www.inc.com/kate-l-harrison/these-5-industries-have-most-to-gain-from-chatbots.html.
“How Secure Are Chatbots?” Chatbots Magazine, Chatbots Magazine, 23 Jan. 2017, chatbotsmagazine.com/how-secure-are-chatbots-2a76f115618d.
Intelligence, Business Insider. “80% Of Businesses Want Chatbots by 2020.” Business Insider, Business Insider, 14 Dec. 2016, www.businessinsider.com/80-of-businesses-want-chatbots-by-2020-2016-12.
Fernandes, Anush. “NLP, NLU, NLG and How Chatbots Work.” Chatbots Life, Chatbots Life, 15 Nov. 2017, chatbotslife.com/nlp-nlu-nlg-and-how-chatbots-work-dd7861dfc9df.
Kelleher, Katy. “Can Instagram Save the Flower Industry?” Observer, Observer, 2 Oct. 2018, observer.com/2018/10/can-instagram-save-the-flower-industry/
Keller, Kevin Lane. Building customer-based brand equity: A blueprint for creating strong brands. Cambridge, MA: Marketing Science Institute, 2001.
“Keller’s Brand Equity ModelBuilding a Powerful Brand.” Strategy Tools From MindTools.com, www.mindtools.com/pages/article/keller-brand-equity-model.htm.
Lola.com. “NLP vs. NLU: What’s the Difference?” Medium, Medium, 5 Oct. 2016, firstname.lastname@example.org/nlp-vs-nlu-whats-the-difference-d91c06780992.
Mikael-Debass, Milena. “Will Chatbots Replace Therapists? We Tested It out.” VICE News, VICE News, 17 Dec. 2018, news.vice.com/en_us/article/nep53m/will-chatbots-replace-therapists-we-tested-it-out.
Moore, Susan. “Gartner Says 25 Percent of Customer Service Operations Will Use Virtual Customer Assistants by 2020.” Gartner, Feb. 2018, www.gartner.com/en/newsroom/press-releases/2018-02-19-gartner-says-25-percent-of-customer-service-operations-will-use-virtual-customer-assistants-by-2020.
“Natural Language Processing (NLP) & Why Chatbots Need It.” Chatbots Magazine, Chatbots Magazine, 25 May 2018, chatbotsmagazine.com/natural-language-processing-nlp-why-chatbots-need-it-a9d98f30ab13.
Nguyen, Mai-Hanh. “The Latest Market Research, Trends & Landscape in the Growing AI Chatbot Industry.” Business Insider, Business Insider, 20 Oct. 2017, www.businessinsider.com/chatbot-market-stats-trends-size-ecosystem-research-2017-10.
O’Hear, Steve. “Cleo, the Chatbot That Wants to Replace Your Banking Apps, Has Stealthily Entered the U.S. – TechCrunch.” TechCrunch, TechCrunch, 20 Mar. 2018, techcrunch.com/2018/03/20/cleo-across-the-pond/.
Raj-Building Chatbots with Python-Using Natural Language Processing and Machine Learning (2019).pdf
Suchman, Lucy A. Plans and Situated Actions: The problem of human-machine communication (Cambridge University Press, 1987)
Sentance, Rebecca. “Cleo, a Chatbot Case Study: Why Brands Need to Be Cautious with Comedy Personas – Econsultancy.” Econsultancy, 22 Feb. 2019, econsultancy.com/cleo-chatbot-financial-services-persona-marketing/.
Stambor, Zak. “How 1-800-Flowers Attracts Millennials.” Digital Commerce 360, 3 Mar. 2017, www.digitalcommerce360.com/2016/03/03/how-1-800-flowers-attracts-millennials/.
Victor, Daniel. “Microsoft Created a Twitter Bot to Learn From Users. It Quickly Became a Racist Jerk.” The New York Times, The New York Times, 24 Mar. 2016, www.nytimes.com/2016/03/25/technology/microsoft-created-a-twitter-bot-to-learn-from-users-it-quickly-became-a-racist-jerk.html.
Wertz, Jia. “Why Chatbots Could Be The Secret Weapon To Elevate Your Customer Experience.” Forbes, Forbes Magazine, 18 Jan. 2019, www.forbes.com/sites/jiawertz/2018/12/23/why-chatbots-could-be-the-secret-weapon-to-elevate-your-customer-experience/#1b17ea384645
Weizenbaum, Joseph. Computer Power and Human Reason (New York: Freeman, 1976)
Weizenbaum, Joseph. “ELIZA – A Computer Program for the Study of Natural Language Communication between Man and Machine,” Communications of the Association for Computing Machinery 9 (1966): 36-45.