jurafsky&martin_3rdEd_17 (1).pdf

440 c hapter 28 d ialog s ystems and c hatbots

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440 C HAPTER 28 D IALOG S YSTEMS AND C HATBOTS Another early system, the GUS system (Bobrow et al., 1977) had by the late 1970s established the main frame-based paradigm that became the dominant indus- trial paradigm for dialog systems for over 30 years. In the 1990s, stochastic models that had first been applied to natural language understanding began to be applied to dialogue slot filling ( Miller et al. 1994 , Pierac- cini et al. 1991 ). By around 2010 the GUS architecture finally began to be widely used commer- cially in phone-based dialogue systems like Apple’s SIRI Bellegarda (2013) and other digital assistants. The rise of the web and online chatbots brought new interest in chatbots and gave rise to corpus-based chatbot architectures around the turn of the century, first using information retrieval models and then in the 2010s, after the rise of deep learning, with sequence-to-sequence models. Exercises 28.1 Write a finite-state automaton for a dialogue manager for checking your bank balance and withdrawing money at an automated teller machine. 28.2 A dispreferred response is a response that has the potential to make a person dispreferred response uncomfortable or embarrassed in the conversational context; the most com- mon example dispreferred responses is turning down a request. People signal their discomfort with having to say no with surface cues (like the word well ), or via significant silence. Try to notice the next time you or someone else utters a dispreferred response, and write down the utterance. What are some other cues in the response that a system might use to detect a dispreferred response? Consider non-verbal cues like eye gaze and body gestures. 28.3 When asked a question to which they aren’t sure they know the answer, peo- ple display their lack of confidence by cues that resemble other dispreferred responses. Try to notice some unsure answers to questions. What are some of the cues? If you have trouble doing this, read Smith and Clark (1993) and listen specifically for the cues they mention. 28.4 Build a VoiceXML dialogue system for giving the current time around the world. The system should ask the user for a city and a time format (24 hour, etc) and should return the current time, properly dealing with time zones. 28.5 Implement a small air-travel help system based on text input. Your system should get constraints from users about a particular flight that they want to take, expressed in natural language, and display possible flights on a screen. Make simplifying assumptions. You may build in a simple flight database or you may use a flight information system on the Web as your backend. 28.6 Augment your previous system to work with speech input through VoiceXML. (Or alternatively, describe the user interface changes you would have to make for it to work via speech over the phone.) What were the major differences?
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