![Образец заголовка What are Chatbots?[1]
• Chatbots are arMficially intelligent computer
systems that converse with humans using natural
language
• IniMally created in the 1960s to simulate human
conversaMon for the purpose of entertainment
• Today, chatbots are abundant and offer
sophisMcated services](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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An Introduction to Chatbots
- 2. Образец заголовка What are Chatbots?[1] • Chatbots are arMficially intelligent computer systems that converse with humans using natural language • IniMally created in the 1960s to simulate human conversaMon for the purpose of entertainment • Today, chatbots are abundant and offer sophisMcated services
- 3. Образец заголовка ELIZA[1] • One of the earliest chatbot systems • Designed to mirror a conversaMon between a paMent (the user) and a psychotherapist (the system) • Uses a simple keyword mapping algorithm – If a keyword is found with the user’s input, an output sentence is selected based upon a rule correlated with that keyword – If no keyword is found, ELIZA returns a default response such as “Please go on.” or “Can you elaborate on that?”
- 4. Образец заголовка ELIZA[1] • For instance, if the input contains the word “sad”, ELIZA can respond, “What’s bothering you?” • Based upon the noMon that if a user menMons a feeling, ELIZA should engage them into opening up about it • ELIZA does not understand what the user says • Generates a response from pre-stored sentences and sentence templates
- 5. Образец заголовка Current Applica+ons of Chatbots[1] • Chatbots have more pracMcal applicaMons due to improvements in data/text mining and machine learning methods • The main purpose of current chatbots is to increase producMvity • Common domains of today’s chatbots are informaMon retrieval (quesMon-and-answer systems), personal assistance, and e-commerce
- 6. Образец заголовка Facebook M • A chatbot service created by Facebook • Beta launched on August 27, 2015 • Few usages of Facebook M are finding restaurant opMons and vacaMon suggesMons • Built right into the messenger; allows users to have easy access in a familiar UI system
- 7. Образец заголовка Siri • Chatbot service by Apple on iOS • Uses voice recogniMon and speaks to the user • Siri has 3 basic funcMons – Task compleMon: Can perform web searches, complete transacMons, make a call, etc… – ConversaMonal intent: Takes into account mulMple contexts such as locaMon and Mme to understand the user’s situaMon – PersonalizaMon: Siri learns about the user and tailors responses to each individual
- 8. Образец заголовка How Do Chatbots Work? • Natural Language Processing • Discourse Analysis • Ontology Learning • Sentence CompleMon
- 10. Образец заголовка Natural Language Processing[2] • Contextually dependent words can be found using Markov Chains – In the sentence, “How is the Apple stock doing?”, the keywords “Apple” and “stock” are extracted • ProbabilisMc language models can be used to assign weighMngs to words These techniques are used in general text informaMon systems. Let’s focus on topics specific to chatbots.
- 12. Образец заголовка Discourse Analysis[3] • Developing a raMonal, coherent discourse from mulMple user inputs • IdenMfies and evaluates pawerns within a series of texts • Creates relaMonships between sentences and enMMes • 3 main models to model discourse – Analysis of semanMcs – Analysis of structure – Analysis of intenMon
- 13. Образец заголовка Discourse Analysis Using Seman+cs[3] • Text is broken down into primiMve units called elements (ex: enMMes, acMons) • Together, elements build semanMc formulas, which represent the meaning of English words Formula for “eat”: – Agent is animate – Object is edible – DirecMon is toward the human mouth • Gives relaMonship between objects and acMons
- 14. Образец заголовка Ontology Learning Creates Models[4] • Chatbots automaMcally create ontologies • An ontology is a model for describing an enMMes properMes and relaMonships • In this example, the system understands that the user likes movies
- 15. Образец заголовка Ontology Learning[4] • Common components of ontologies include: – Individuals: instances or objects (the basic or “ground level” objects) – Classes: sets, collecMons, concepts, classes in programming, kinds of things – Awributes: aspects, features, properMes, characterisMcs – RelaMons: ways in which classes and individuals can interact with each other • The more the user interacts with the chatbot, the stronger the ontologies it can create
- 16. Образец заголовка Genera+ng A Response[5] • Now that the chatbot system has understood the user’s input, it must generate an appropriate response • Similar to informaMon retrieval: rather than matching the user’s “query” to a document, we are mapping to one to two sentences • We take into account certain stop words such as “you”, “I”, and “because” that may be important to the semanMcs of the sentence • The system ranks relevant sentences based on a probabilisMc funcMon and selects the highest one
- 17. Образец заголовка Sentence Genera+on[5] • Chatbots should keep a copy of the conversaMon in memory • Prevents the chatbot from repeaMng the same response if the user sends in the same input mulMple Mmes • Feedback based upon – Relevance of response to user input – SyntacMcal and semanMc correctness
- 18. Образец заголовка Sentence Genera+on[5] • What about sentences that are not in the system’s database? • One method to solve this uses a geneMc algorithm to crossover exisMng sentences to produce a new response • To implement this, we can use the concept of largest common paRern • LCP(s, t) = (p1, p2, …, pn) where n = 1 and p1 = Ø OR • LCP(s, t) = (p1, p2, ..., pn) where for every 1 ≤ i ≤ n, pi ≠ Ø and s and t are sentences: – s = s1 p1 s2 p2 … sn pn – T = t1 p1 t2 p2 ... tn pn
- 19. Образец заголовка Sentence Genera+on[5] • s★, t★ = first word of each sentence • ss★, tt★ = each sentence without the first word Auer finding the LCP, we can define the complement vector of each sentence: • Inds = (s1, s2, …, sn) • Indt = (t1, t2, ..., tn)
- 20. Образец заголовка Sentence Genera+on Example[5] • s = Chatbots can simplify many tasks for you • t = I can finish many assignments for school today • LCP(s, t) = (can, many, for) • Inds = (Chatbots, simplify, tasks, you) • Indt = (I, finish, assignments, school today) Assume we randomly decide to swap genes 2 and 3: • Child 1 = Chatbots can finish many assignments for you • Child 2 = I can simplify many tasks for school today It is possibly that a generated sentence is not syntacMcally or semanMcally correct.
- 21. Образец заголовка Evalua+ng A Chatbot’s Performance[6] • Evaluated based on similarity to fluent, human conversaMon • Turing test – Judges chat with mulMple chatbots and score them in their ability to simulate a human’s natural language • However, “naturalness” is subjecMve • Important to take into account quanMtaMve data to measure efficiency
- 22. Образец заголовка Evalua+on Metrics[6] • QuanMtaMve – Time to develop response – Time/number of interacMons to complete user’s task – Number of re-prompts – Number of irrelevant system responses • QualitaMve – Naturalness – Clarity – Friendliness – User saMsfacMon
- 23. Образец заголовка Limita+ons to Chatbots • Chatbots require a high computaMonal complexity to funcMon efficiently • Languages have mulMple dialects and varying sentence structures that make it difficult for chatbots to properly understand the user • Chatbots have difficulty answering abstract quesMons • Chatbots cannot easily recognize humor and sarcasm
- 24. Образец заголовка The Future of Chatbots • The popularity of chatbots is rapidly rising • Businesses in various industries are starMng to implement chatbots to make their services more seamless for the user • Users rather have a conversaMon than click buwons and fill out forms • They will be used to simplify online processes: everything from simulaMng customer service representaMves to helping you order food • No chatbot has uncontroversially passed the Turing test yet
- 25. Образец заголовка Deep Learning: The Future • Deep Learning is a branch of machine learning based on a set of algorithms that awempt to model high-level abstracMons in data by using mulMple processing layers Models of Deep Learning • Retrieval-based models (easier) – Use a repository of predefined responses and some kind of heurisMc to pick an appropriate response based on the input and context – The heurisMc could be as simple as a rule-based expression match, or as complex as an ensemble of machine learning classifiers – Don’t generate any new text, they just pick a response from a fixed set • Genera+ve models (harder) – Don’t rely on pre-defined responses – Generate new responses from scratch – Based on machine translaMon techniques; instead of translaMng from one language to another, we “translate” from an input to an output