Introduction

the process of creating a character is iterative in nature. The following steps create an initial character that will probably need to be refined by repeating the steps as required to obtain the desired behavior.

Three actions are required to create the content necessary to drive the natural language component:

1. Create an initial set of system and user utterances. Each utterance needs to be associated to an identifying string (like a name) and the natural language understanding and dialogue management modules will use these identifiers instead of the real utterances.
2. Create a dialogue policy. This policy consists of:
   
   1. the set of variables that constitute the dialogue state (the dialogue manager is an information based one. That is it decides what to say based on what the user said and the current state of the conversation as represented in the dialogue state (also called information state)).
   2. the set of sub-dialogue networks. These sub-dialogues are similar to planning operators, with preconditions and effects. The dialogue manager (DM), will select one based on what the user said and the current dialogue state. Each sub-dialogue typically carries out a short portion of an entire dialogue (e.g. the greeting phase, or answering a question).
3. Train the natural language understanding module to map a given utterance to one of the known identifying strings defined in the first step.

The entire information that defines a character, e.g. CakeVendor, for the FLoReS system is defined in a set of files sitting in the directory resources/characters/CakeVendor/

This directory contains three sub-directories that parallel the 3 steps defined above:

• content: this directory contains the files that define the user and system utterances and their identifying strings.
• dm: this directory contains the dialogue policy
• nlu: this directory contains the natural language understanding model learned by the corresponding module from the data in the data found in the content directory.

Step 1: Authoring the content

Authoring the content consists of editing 2 files. One for the user utterances and one for the system utterances. The file that contains the user utterances is basically the training data for the natural language understanding (NLU) module. The system utterance file instead contains the utterances that the character can say.

User utterances:

The NLU module given an utterance returns the most probably identifying strings. It's based on a maximum entropy multiclass classifier and therefore the user utterance file should list utterances maintaining their natural frequency. That is, the best way to obtain these utterances is by running wizard of oz experiments or role plays. Then annotate the data by assigning to each utterance said by a user during these experiments an identifying string. These identifying strings are sometime called speech acts or dialogue acts (in case more domain specific semantic is attached to the basic speech act). Examples of dialogue acts are: question.age to mark all utterances in which the user is asking about the age of the addressee.

When we design a dialogue policy for the character using this content, whenever we want to wait for the user to say a certain utterance, we will use the string identifier (speech act) associated to that utterance.

System utterances:

these uttearnces

File format:

The user and system utterances files use the same Excel spreadsheet format. These files have a number of columns (these are the initial 2 rows of the system utterances file for the character used in the example below):

The only 2 columns of relevance are SPEECH_ACT and TEXT.

SPEECH_ACT contains the string identifier for the corresponding utterance found in the TEXT column.

The user utterance file needs to be called user-utterances.xlsx and the system utterance file must be called system-utterances.xlsx (these names can be configured, but the default configuration looks for those names in each character available).

Step 2: Authoring the dialogue policy

Step 3: Train the natural language understanding module

An example

CakeVendor.zip contains all is required to define a CakeVendor character that is an extension of the character created in this other tutorial for NPCEditor.