CONVERSE

Intelligent Research Ltd.David Levy, Bobby Batacharia

University of SheffieldYorick Wilks, Roberta Catizone, Alex Krotov

Converse

What is CONVERSE?

• A conversation program designed for the Loebner competition in 1997.

• What is the Loebner competion?– Turing test where a number of judges converse

with the submitted programs using a keyboard. They then decide which of the programs is the most real (One of the entrants is a real person sitting behind a terminal in another room).

• We won

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What is CONVERSE

• Script driven system that tries to keep control of the conversation by posing questions to the user.

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Character Personality

• Catherine, a 26 year-old female journalist

• Born in Britain, living in New York

• We store information about Catherine in a Database.

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Technical Details

• Written in C and C++

• Runs under Windows 95

• Modular System Architecture

• Can converse on 60 different topics– Abortion, work, travel, death, religion, gun-

control...

• Speech component was added later for output

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System Architecture

• Input Modules

• Data Modules

• Action Modules

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Input Modules

• Pre-processing

• Parser

• Micro-Queries

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Preprocessing

• Spell checker

• Elision expansion

• Name tagger

• Punctuation correction

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Parser

• Prospero Parser (off the shelf)– Tags part of speech– Parses the input

• Parser Post Processor (PPP)– Flattens the parse tree– Corrects deficiencies of Prospero

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MicroQueries

• Defines a set of patterns (to be mapped on to the scripts eventually).

• Individual patterns are connected to each other with logical functions.

• Access to the input as well as Wordnet functions.

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Data Modules

• Person Database (PDB)– Stores all the information for each user– Stores information about the system character

• Wordnet– Synonyms and word definitions

• Collins– Famous people, places and things

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Action Modules

• Where-to-Go

• Pisces

• Special Cases

• Scripts

• Information Scavenger

• Topic Change

• Generator

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Where-to Go Module

• Dispatcher module that decides which of the action modules should handle an utterance.

• Each action module places a bid (indicating the confidence that it has a valid response) and the highest bid wins.

• An action module can extract data from the input and store it in the database.

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Pisces

• Question Answering Module

• Divided into 2 parts - question recognition and question answering

• Contains twenty different question types

• Questions are answered by first consulting the person database

• Wordnet is used to enrich the search and for definitions.

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Pisces

• Collins is used for famous entities - people and places.

• If a response cannot be formulated, then a filler response is given. The filler responses are typed according to the question type.

• We distinguish WH questions from other question types.

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Special Cases

• Handles exceptional cases– rude language– illogical birthdays or age– Talk about sex– Violence

• A response is triggered from a set of canned phrases for the above categories.

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Script Module

• Guides the conversation through a network of script lines

• Each script line has – Some canned text– Links to other related scripts and – A set of Microqueries (patterns that map onto the

scripts)

• Keeps track of what the system has said to avoid repeats.

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Information Scavenger

• Extracts any possible information from an user utterance if it is a statement.

• It is only activated if no other action modules have extracted information from the current utterance.

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Topic Change Module

• Controls the flow of conversation

• Decides what order the available 60 topics should be presented to the user.

• Can be triggered by the user.– The system uses content words from the user’s

response to try to match to topic keywords

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Generator

• Takes the results of the action modules and assembles then into a coherent utterance.

• Uses information from the Person Database where necessary.

• Connectives are added where need to ensure a smooth flowing respons.

• Consults a module that provides a number of different ways to say the same thing.

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Limitations

• Parser not meant for dialogue

• We don’t really take into account the user’s previous utterances even though we have kept a dialogue history.

• Doesn’t have an inference engine.

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Advantages

• Flexible design - modular architecture

• Person database allows for swapping of different system personalities

• Has hooks for incorporating large data resources (encyclopedias, knowledge bases etc.)

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Applications

• Virtual friend

• Toy interface

• Front-end to a relational Database

• Front-end to a software help system