Module 3
Chatbots - Natural Language Processing
"We no longer teach people how to communicate with systems, we are teaching systems to communicate with people."
"We no longer teach people how to communicate with systems, we are teaching systems to communicate with people."
In this module, different types of chatbots are illustrated by applications. The students learn what chatbots are and how they work. Questions are asked, for example how programmers manage to make chatbots appear "human" or "intelligent" or why understanding human language is actually not that easy.
Students will be able to
| Time | Content | Material |
|---|---|---|
| 10 min | Analysis of failed chatbot conversations | Slides |
| 10 min | Introduction to Chatbots | Slides |
| 30 min | Testing the limits of a chatbot | Slides |
| 30 min | Build Paper Chatbots (Click-Structure) | Slides |
| 30 min | Build Paper NLP Chatbots | Slides |
In this module, the students are thrown directly into the topic. At the beginning, conversations between a human user and a chatbot are shown, where the conversation did not succeed.
Through this first confrontation, the students are activated for this topic and can roughly imagine what can be understood by the term chatbots.
For most of these example-conversations, the chatbot's predefined word recognition is not sufficient enough for it to respond adequately to the user. The constant non-recognition frustrates users and chatbots are less well accepted by people.
With the help of these first few examples, the students can see how complex human language can be and how many things have to be taken into account when developing a chatbot.
After this first short confrontation and discussion of the course of the conversation, the students should work on further questions in the plenum. The main thing here is personal experience and personal access to chatbots. The students should collect which chatbots they know and which they use themselves (do they use any at all?). The teacher can present chatbots from the different areas as input. Furthermore, it is about clarifying important questions about chatbots. What exactly are chatbots and what problems could arise with them?
To go deeper, further socially and ethically interesting questions can be worked on, which revolve around whether chatbots are a good alternative for lonely and depressed people and whether it should be obvious that the conversation partner is non-human.
The term "chatbot" is derived from "to chat" and "robot", i.e. a robot that can talk to you. It can answer questions automatically, without further human help, search for information on the Internet and much more. So that chatbots doesn't look so "artificial", companies sometimes use avatars, which are small images or animations, to give the chatbots an appearance. Many chatbots today use a variety of AI applications to make conversations run as smoothly as possible. This allows bots to store information and use it to teach themselves things. But still, if chatbots do not know what to do, they may pass the user on to a human colleague.
Chatbots come in many different forms. Mainly chatbots are developed for reasons of efficiency, to get information quickly (e.g. when will the new kitchen cabinets be available from Ikea? Or can I cancel my order from Lieferando?). Some can wire money, others can schedule doctor appointments for you, or order food for you. And new features are constantly being added. Here are more examples of chatbots:
In themselves, chatbots are harmless, even if that comes across differently in some science fiction films (comparing narrow and wide AI). However, with a large amount of stored data there is always a risk that someone will steal this information and use it for criminal purposes. Feeding in the wrong data can also result in racist or discriminatory bots, such as the Twitter bot tay (cf. bias errors in the ethics module).
In particular, however, loneliness seems to be an increasingly present phenomenon today. More and more people prefer to communicate digitally than to talk in real life. Chatbots are mentioned as an extraordinary example. Bots like Mitsuku or Xiaoice talk to millions of users and are able to incorporate information from previous conversations. But communication with chatbots should not only be presented negatively. New technologies flowing into chatbots can help people out of loneliness. Especially in current times, when people find themselves in quarantine again and again, but also older people or those who don't know who to talk to about problems and worries, chatbots can help them find their way out of loneliness and against mental health problems such as depression or help anxiety disorders. But can these technologies actually replace human contact?
"Mitsuku doesn't pretend to be able to replace a real person, but she's always available if anyone needs her, instead of talking to the four walls," says Steve Worswick, the developer of Mitsuku. These chatbots differ from voice assistants like Siri and Alexa in that they respond to user input in a friendly and empathetic manner.
A well-known example is the Chinese chatbot Xiaoice, which has over 660 million users around the world and responds to messages in a sensitive, almost human way. Users of the bot let Xiaoice cheer them up, tell jokes or write all their worries away while the bot listens carefully.
Another extremely exciting project is the App Replika. Replika is an artificial intelligence trying to create a digital copy of your personality - a bot trying to chat like you. "Replika writes with you. Asks questions. take care of you Wants to be the friend you've always wanted."
Why are chatbots used? What are their advantages? Discuss in groups!
Have you already come into contact with chatbots? If so, for what purpose?
What problems or dangers could there be with chatbots?
Possible further questions:
Chatbots are also used with lonely people or people with mental health problems. Do you think that makes sense or is well accepted by those affected? What problems could arise when lonely people interact more (or only) with chatbots?
With some conversations you don't know exactly whether you're talking to a human being or a chatbot. Should chatbots be clearly marked as such?
In this phase of the module, the students should talk directly to a chatbot and find out for themselves what chatbots are capable of and what their limitations are.
Mitsuku (nickname "Kuki") would be particularly interesting for this exercise. This application has already won the Loebner Prize five times and is therefore good at providing authentic answers.
In order to use Kuki, however, a free account must be created, which can also be used to save the conversation histories for future reference. Furthermore, Kuki is unfortunately only available in English and answers very evasively in other languages, which means that there is no point in the students doing complete testing in other languages (but of course it would be exciting to let the students try that too).
If it makes no sense to create accounts for the lesson or there are no resources available for testing with Mitsuku, a paper exercise can be done as an alternative, in which the students can evaluate excerpts from the test talks for the Loebner Prize themselves. The ratings of the jury can be shown later and the results of the students can be discussed.
The Loebner Prize is awarded to programs that pass the Turing test. There are three categories for this award: Bronze Medal: for the program that proves to be the most "human-like" (awarded annually), Silver Medal: the program passes the written Turing test, and Gold Medal: should the program pass the total Turing test. Pass the test in which multimedia content such as music, speech, images and videos must also be processed. So far, no program has passed the Turing test. So far, only the bronze medal has ever been awarded.
With the conversation between the students and Mitsuku, a very simplified version of the Turing test is carried out.
A test developed by Alan Turing in 1950 to determine how well a program can mimic a human's language. In the Turing test, a person must be able to determine several times without error whether an answer to a question was given by a computer or by another human. If the person cannot do this, the computer has "passed" the test.
In our case, the students already know that they will be conversing with a chatbot and, from this perspective, will try to find out how humanely a chatbot can answer and make initial assumptions as to how chatbots can "understand" our human questions and answers.
In this exercise you will take a closer look at the chatbot Mitsuku (nickname Kuki): https://chat.kuki.ai
Stop the time!
How long does it take for you to realize that this isn't human? How did you recognize that this is a computer system?
In this shortened exercise, the students can work with statements that Mitsuku made in the Turing test by the Loebner Prize jury. This exercise can simply be printed out and filled out by the students, the jury's rating can finally be displayed with the PowerPoint or written separately on the blackboard.
Steve Worswick's Mitsuku is an apparently 20 year old chatbot and was subjected to the Turing test in a competition.
Mitsuku was asked 20 questions, some you can find in the table below.
Read the questions and their answers carefully.
In partner work, discuss how “human” the different ones are for you.
Answers are, and give points for each answer:
In this last major work phase, the students will now try to design the theoretical basis for a chatbot themselves. The programming of an actual chatbot as such is not intended in this module, since it would go beyond the time frame and this is only about the basic understanding of how to actually build a chatbot.
In this module, two different approaches to constructing chatbots are examined in more detail. You can theoretically do both parts or, if time is short or you want to decide on a level of difficulty, you can do one of the two paper chatbot exercises.
Basically, we are dealing with two different construction options here:
In these two exercises, the students will recognize that in order to be able to provide a fluent, authentic conversation, a large number of eventualities must be covered.
In this exercise, students develop a flowchart-based chatbot using poster paper, pens, and post-its. It would also be possible to work digitally with mind map tools or websites such as Canva or Miro .
Decision tree chatbots are the most unchatty kind of chatbots and are preprogrammed to follow a sequence, which can be very simple or complex. This chatbot works using pre-selected widgets with button options. It allows you to get creative with your chatbot's text and display options, but your user is expected to choose between these options that you define. Companies use them because they're cheaper to build, quicker to deploy and can still be useful, entertaining and educational.
At the beginning of this exercise, the students should think about the purpose of the chatbot (should you be able to use it to book hairdresser appointments, ask about the weather or order pizza…).
If you have more time when building your chatbot, you can also consider the personality of your chatbot. The character of the chatbot does not have to be highly complex. An authentic, humorous chatbot can strengthen the trust of users and is also more fun to use. Is your chatbot a quick-witted, middle-aged woman, or a small, shy boy, or a serious older man? Keep in mind which company your chatbot represents so that the essence of your chatbot fits your fictional brand.
When your chatbot is finished, exchange ideas with another group and play through the chatbot! Does everything make sense or are there problems?
In this exercise, students develop a paper chatbot that reacts to keywords like NLP models and thereby gives correct answers. the students receive a table with possible input and output words and have to respond appropriately to the given input. the students will soon find out that a large field of possibilities has to be covered and that spare keys have to come into play so that a conversation can run smoothly.
When using a Natural Language Processing (NLP) Chatbot you have to enter a verbal input using the keyboard or saying it out loud. The bot analyses your words and turns them into information. It consists of understanding the human language, Natural Language Understanding (NLU) and creating the language Natural Language Generation (NLG).
A chatbot sees what letters you write but has no knowledge of the meaning of the words. The meaning of the phrase "I want to make an appointment" is perfectly clear to you, but to a computer it's just a list of letters. First, it means as much to him as “mis sdaijhw wek” does to you.
Let's say we have a chatbot that books and cancels appointments at a hair salon. The chatbot asks "How can I help you at our Hair Salon?" and the user replies “I want to book an appointment.”. So, this response is the input that the chatbot receives. The chatbot now starts a step-by-step process (also called a pipeline) to recognize the necessary structures so that the program can give the correct answer.
Language assistants such as Alexa and Google Assistant also work in this way. The speech recognition is carried out there first. This converts the microphone signal into a character string. After that, the same creation of structures is performed.
In 2018, Google's "Duplex" application was the first to authentically arrange appointments at the hairdresser's or in a restaurant. Duplex's voice sounds so convincingly human that many initially thought the videos of these conversations were fake.
Google boss Sundar Pichai explained that these recordings were real but only the very least conversations went so perfectly like these. Most conversations with the AI still fail miserably.
A video of one of the successful phone calls can be found in the references.
Have a conversation with the paper chatbot!
As you have heard before, it takes a lot of steps that we humans take for granted to understand a statement.
Try to solve the following tasks:
In this last step within this module, language as a whole will be discussed again. By exploring existing applications but also by developing their own paper chatbots, the students have developed an understanding of how complex language can be and developing a program that can respond to it.
The human language is a challenge for artificial intelligence for sure. "With all its ambiguities, nuances and misunderstandings, it is probably the most complex system that humans have ever developed." That's why no one has yet succeeded in constructing a machine that simulates a credible human interlocutor.
But, as the last picture of the module presentation shows, the construct of language often does not work even between people. Models such as Schulz von Thun's four-page model can also be embedded in lessons to show how complex language can be for people and that the expectations between sender and receiver can often vary greatly.
