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The Learning Chatbot

The Learning Chatbot. Bonnie Chantarotwong IMS-256 Fall 2006. What is wrong with state of the art chatbots?. They are repetitive They are predictable simple pattern matching & set response They have no memory can lead to circular conversations They don’t sound like real people.

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The Learning Chatbot

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  1. The Learning Chatbot Bonnie Chantarotwong IMS-256 Fall 2006

  2. What is wrong with state of the art chatbots? • They are repetitive • They are predictable • simple pattern matching & set response • They have no memory • can lead to circular conversations • They don’t sound like real people

  3. Proposed Solution • Train chatbots on a corpora of conversations in order to mimic a given personality

  4. Filtering the training corpus • Need a lot of conversations containing query screen name • Eliminate undesirable data • phone numbers • Addresses • sensitive gossip • Eliminate highly technical data • since most tech problems are very specific, unless the bot was trained on a newsgroup, learned responses are not likely to be useful for tech support

  5. Parsing the training corpus • Extract messages from HTML • Group together consecutive messages by the same screen name • Simplify prompt messages • !!!!!!!??????? -> !? • Ohhhhhhhhhhhh! -> ohh! • WhATz uP?? -> whatz up? • hahahahaha -> haha • Break prompts into word sequences (eliminating stop words) • I tookthecat toavet -> [i, took, cat, to, vet]

  6. Constructing the CFD • CFD conditions are prompt words • FD samples are string responses, with numeric count indicating strength of correlation • Example: • Cfd[‘sleep’].sorted_samples() -> [“sleep is the best thing ever”, “are you tired?”, “maybe after I eat.”, “hang on a sec.”]

  7. Constructing the CFD Simple Concept: If a prompt is n words long, then each word is 1/n likely to have caused the response i 1/3 Can we put mittens on it? 1/3 want 1/3 kitten 1/3 1/3 Me too, I’m hungry. food 1/3 1/6 they 1/6 1/6 What kind did they have? had 1/6 good 1/6 1/6 at restaurant Original Conversation:A: I wantakittenB: Can we put mittens on it?A: I want foodB: Me too, I’m hungryA: They had good food attherestaurantB: What kind did they have?

  8. Using the CFD Problem • Each word in a prompt is not equally likely to have caused the response • More common words (such as ‘I’) are less indicative of meaning Solution • Take into account the commonality of the word over all conversations • Divide the weight of the word/response pair by the weight sum over all samples for that word • Rare words are weighted more; using a dynamic scale • This improved quality of bot responses greatly!

  9. Using the CFD - Example CFD: Cfd[‘i’] = [(“Can we put mittens on it?”, 1/3), (“Me too, I’m hungry”, 1/3)] Sum: 2/3 Cfd[‘want’] = [(“Can we put mittens on it?”, 1/3), (“Me too, I’m hungry”, 1/3)] Sum: 2/3 Cfd[‘kitten’] = [(“Can we put mittens on it?”, 1/3)] Sum: 1/3 Cfd[‘food’] = [ (“Me too, I’m hungry”, 1/3), (“What kind of food did they have?”, 1/6)] Sum: 1/2 Cfd[‘they’] = [ (“What kind of food did they have?”, 1/6)] Sum: 1/6 Cfd[‘had’] = [ (“What kind of food did they have?”, 1/6)] Sum: 1/6 Cfd[‘good’] = [ (“What kind of food did they have?”, 1/6)] Sum: 1/6 Cfd[‘at’] = [ (“What kind of food did they have?”, 1/6)] Sum: 1/6 Cfd[‘restaurant’] = [ (“What kind of food did they have?”, 1/6)] Sum: 1/6 Responses (“how is your kitten?”) = [(“Can we put mittens on it?”, (1/3 / 1/3) = 1)] Responses (“the food was good”) = [(“Me too, I’m hungry”, (1/3 / 1/2) = 2/3), (“What kind of food did they have?”, (1/6 / 1/2 + 1/6 / 1/6) = 4/3]

  10. Using the CFD Responses (“the food was good”) = [(“Me too, I’m hungry”, (1/3 / 1/2) = 2/3), (“What kind of food did they have?”, (1/6 / 1/2 + 1/6 / 1/6) = 4/3] • Given the CFD, the response to any prompts containing ‘food’ and ‘good’ will give back “What kind of food did they have?” • Problem: This can lead to redundancy • A: The food was good • B: What kind of food did they have? • A: Didn’t you think the food was good? • B: What kind of food did they have? • Solution: Store an FD of used responses, and don’t use them again • A: The food was good • B:What kind of food did they have? • A: Didn’t you think the food was good? • B: Me too I’m hungry

  11. What if we have no responses? Because: • We’ve never encountered any of the prompt words • We’ve used up all relevant responses We can: • Find a random response • Enhance randomness by favoring unlikely responses (near the end of association lists) to reduce redundancy • Fabricate a response based on pattern matching • Select a response from a default list of responses (i.e. “Lets talk about something else”, “I don’t know anything about that” * All my bots implement 1 & 2, and one bot (bonnie) also implements 3 & 4

  12. Interactive Webpages are not trivial • Especially if you want to retain some “memory” of the past • First CGI problem: A new bot is created with every web prompt - all memory is lost • Solution: • Write all bot state changes to a file, including used responses. • Run this file with every prompt, and reset it when a new conversation starts • The bot loads the huge CFD & all state changes from scratch with EVERY call. Slow, but works.

  13. Interactive Webpages are not trivial Memory = Self-modifying code

  14. Interactive Webpages are not trivial Memory = Self-modifying code

  15. Demo http://ischool.berkeley.edu/~bonniejc/

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