用NLTK库实现一个text-based conversational agent原型产品。
Introduction
In this assignment you will implement a prototype of a text-based
conversational agent = dialogue system that is able to converse with a human
in ordinary natural language and provide rail information for a Sydney
suburban line Northern Line. The user should be able to ask for timetable
information and information about planned trackwork for the fortnight ahead.
The assignment consists of a basic task where the conversational agent is
completely in control of the conversation and an advanced task in which the
conversation shows some form of mixed-initiative between the conversational
agent and the human as discussed in the lecture. For the implementation of
your agent you have to use Python 3.6.x and SQLite and optionally if you wish
you can use the Natural Language Toolkit NLTK.Please note: the aim of this
assignment is to implement a goal-oriented conversational agent andnota
chatbot.
Basic Task
For this basic task you have to implement a conversational agent who is always
in control and drives the conversation. Your agent should be able to handle
the following two dialogues timetable information 2.1 and trackwork
information 2.2 that we will use to test your implementation. But of course
you don’t want to hard-code these dialogues and you should allow for different
trips and destinations on the Northern Line. Also you should use a grammar
most likely implemented via regular expressions or string methods that allows
for some variation in the user input. The agent should recover gracefully if
something goes wrong in a dialogue state and re-prompt the user accordingly.
Timetable Information
- Computer: Welcome to Sydney Train’s text-based rail information service. This service will help you finding convenient ways to travel by asking you a number of questions. If you are not sure about how to answer a question simply type ‘help’. You can ‘quit’ the conversation anytime.
- Computer: Would you like timetable information or information on trackwork?
- User: I would like timetable information.
- Computer: Sure. What station would you like to leave from?
- User: Epping.
- Computer: Okay Epping. And where are you travelling to?
- User: Hmm - to Central Station.
- Computer: Okay to Central and do you want to travel on a weekday or on a weekend?
- User: I want to travel next Monday.
- Computer: Travelling on Monday September 18 and what time would you like to depart from Epping?
- User: At about 10:45 or so in the morning.
- Computer: Let me see - I have a train leaving Epping at 10:47 and arriving at Central at 11:23. Would you like an earlier or later train?
- User: A later one please.
- Computer: Let me check - I have a train leaving Epping at 11:00 and arriving at Central at 11:25. Would you like a later train?
- User: No thank you - this train suits me well.
- Computer: Okay thank you for tavelling with Sydney Train - cost effective reliable and convenient.
Trackwork Information
- Computer: Welcome to Sydney Train’s text-based rail information service. This service will help you finding convenient ways to travel by asking you a number of questions. If you are not sure about how to answer a question simply type ‘help’. You can ‘quit’ the conversation anytime.
- Computer: Would you like timetable information or information on trackwork”
- User: Trackwork information please.
- Computer: On what line are you planning to travel on?
- User: Northern Line.
- Computer: On what day will you be travelling?
- User: On the sixteenth.
- Computer: At what time?
- User: 10:00.
- Computer: There is trackwork on the Northern Line on Saturday 16 September 02:00 to Monday 18 September 02:00 Buses replace trains between Hornsby and Strathfield via Eastwood.
- Computer: Would you like more information on timetable or trackwork?
- User: Timetable please.
- Computer: Sure. What station would you like to leave from?
Note that the welcome prompt should only be used the first time and the user
should be able to select between “timetable information” and “trackwork
information”.
Note also that a dialogue state has the following general form: - Prompts:
- Main prompt: Would you like timetable information or information on trackwork”
- Secondary prompt: Type either timetable or trackwork.
- Help prompt: This service can provide information on train timetable or trackwork information. Please type either timetable or trackwork.
- Expected Response: timetable|trackwork
- Grammar: Format of your choice most likely regular expressions or string methods
- Event Handling: For example if the answer is not in the scope of the grammar then the user should be re-prompted or if the user types ‘help’ then the help prompt should be used. In certain states the agent needs to connect to the database and extract the relevant information from there.
This basic version of the conversational agent should be implemented as a
Python program with the name train-1.py and the dialogue should run in the
Python shell. Together with your main program you have to submit all
additional non-standard modules that are necessary to run your code.
Advanced Task
For the advanced task you can take the work of the basic task as starting
point and extend it. In the basic version the agent is completely in control
of the conversation and collects in each dialogue state one particular piece
of information. However this is not a very satisfactory solution: your
dialogue design should allow for some degree of mixed-initiative for example:
- Computer: What train trip do you want to make”
- User: I wanna travel from Epping to Central.
Here you have to collect two pieces of information during a dialogue state.
How far you take this approach is up to you and you can come up with your own
prompts in this advanced task. However keep in mind that you want to strive
for a high task completion rate and give the user an good experience. You can
implement your own SQLite queries for this advanced task if you like and
extend the database if you like and add additional features of your choice to
the conversational agent. If you choose to add additional features to the
conversational agent you will need to document these features in a two-page
report and explain why these feature add value to your system. This advanced
version of the conversational agent should be implemented as a Python program
with the name train-2.py and the dialogue should run in the Python shell.
Together with your main program you have to submit all additional non-standard
modules that are necessary to run the advanced version of the code.
SQLite Database
In order to converse with the agent you have to access an small SQLite
database train.db where you can find the relevant information for the basic
task. Since this assignment is not about databases and database design we
provide only a very simple database which you can find under Assignment 1:
Train Database together with a Python program train-db.py that has been used
to generate the database.The database contains only information for
connections on the Northern Line between about 10:20 and 12:20. Note that the
following train stations are part of the Northern Line this information is
important because these names will be part – among other things – of your
grammar.
- Hornsby
- Normanhurst
- Thornleigh
- Pennant Hills
- Beecroft
- Cheltenham
- Epping
- Eastwood
- Denistone
- West Ryde
- Meadowbank
- Rhodes
- Concord West
- North Strathfield
- Strathfield
- Burwood
- Redfern
- Central
- Town Hall
- Wynyard
- Milsons Point
- North Sydney
- Waverton
- Wollstonecraft
- St Leonards
- Artarmon
- Chatswood
For the basic task of this assignment you don’t have write any SQL queries
from scratch. The following two SQL queries answer the questions raised in the
basic task where the user is looking for a train from Epping to Central that
leaves Epping at around 10:45 on a weekday WD note the code for weekend is WE
and whether there is some trackwork on the Northern Line on 16 August at 10:00
or not. You can use these SQL queries as templates for other similar queries.
As you can see there exists a train that leaves Epping at 10:47 and arrives at
Central at 11:23. This is the closest match to the SQL query. There exist
earlier and later trains.
The second SQL query is pretty straightforward and returns the relevant
trackwork information:
You can find an introduction to SQLite in this tutorial.
The basic task of the assignment is worth 14 marks and the advanced task is
worth an additional 6 marks. You will be awarded marks according to the
following guidelines:
Basic Task
Functionality: The conversational agent processes both dialogues as
illustrated above re-prompts the user if the input was not recognised and
provides help if required for each dialogue state.
Grammar coverage: The grammar regular expressions/string methods shows some
sophistication in pattern matching.
Quality of the code: The Python code is modular well-documented and uses a
consistent naming convention.
Advanced Task
Quality of prompts: The prompts have been selected carefully to implement a
form of mixed-initiative.
Additional features and report: The additional features at most two are useful
add value to the conversational agent and are carefully implemented 2 marks.
The choice of the additional features is well-motivated and clearly described
in the report 2 marks.
Submission
You have to submit a ZIP file comp329-assignment-1.zip that contains the
Python program train-1.py for the basic version of the conversational agent
and the Python program train-2.py for the advanced version incl. all non-
standard modules and the SQLite database that are necessary to run your code.
Your code needs to run out-of-the-box we only assume a basic Python
installation together with the NLTK toolkit. If you submit a solution to the
advanced task of the assignment that contains additional features then you
have to add a twopage report in form of a PDF file to the ZIP file of your
submission.
