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Plato Research Dialogue System

This is v0.3.1

The Plato Research Dialogue System is a flexible framework that can be used tocreate, train, and evaluate conversational AI agents in various environments.It supports interactions through speech, text, or dialogue acts and eachconversational agent can interact with data, human users, or otherconversational agents (in a multi-agent setting). Every component of everyagent can be trained independently online or offline and Plato provides aneasy way of wrapping around virtually any existing model, as long as Plato'sinterface is adhered to.

Publication citations:

Alexandros Papangelis, Mahdi Namazifar, Chandra Khatri, Yi-Chia Wang,Piero Molino, and Gokhan Tur, "Plato Dialogue System: A Flexible conversationalAI Research Platform", ArXiv Preprint [paper]

Alexandros Papangelis, Yi-Chia Wang, Piero Molino, and Gokhan Tur,“Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning”,SIGDIAL 2019 [paper]

Plato wrote several dialogues between characters who argue on a topic by askingquestions. Many of these dialogues feature Socrates including Socrates' trial.(Socrates was acquitted in a new trial held in Athens, Greece on May 25th 2012).

News

v0.2: The main update from v0.1 is that Plato RDS is now provided as a package. Thismakes it easier to create and maintain new conversational AI applications andall tutorials have been updated to reflect this. Plato now also comes with anoptional GUI. Enjoy!

ReadMe Contents

• How does the Plato Research Dialogue System work?
• Installation
  • Installing Plato from source code (Recommended)
  • Common Issues During Installation
• Running Plato
• Building a Conversational Agent with Plato
  • Domain
  • Controller
  • Agent
    • Rule-based modules
    • Trained modules
      • Plato internal experience
      • Parsing data with Plato
      • Training Components of Conversational Agents
        • Train NLU
        • Train DST
        • Train Dialogue Policy
          • Supervised Dialogue Policy
          • Reinforce Dialogue Policy
        • Train NLG
        • Train an end-to-end conversational agent based on Seq2Seq
    • Training Multiple Conversational Agents Simultaneously
    • User Simulators
      • Agenda-Based User Simulator
      • Dialogue Act to Language User Simulator
    • Create a new module
      • Create new States, Actions, Learning Algorithms, and more
  • Graphical User Interface
• Upcoming Tutorials
• Upcoming Features
• Acknowledgements
• Conclusion

How does the Plato Research Dialogue System work?

Conceptually, a conversational agent needs to go through various steps in orderto process information it receives as input (e.g., “What’s the weather liketoday?”) and produce an appropriate output (“Windy but not too cold.”). Theprimary steps, which correspond to the main components of a standardarchitecture (see Figure 1), are:

• Speech recognition (transcribe speech to text)
• Language understanding (extract meaning from that text)
• State tracking (aggregate information about what has been said and done so far)
• API call (search a database, query an API, etc.)
• Dialogue policy (generate abstract meaning of agent’s response)
• Language generation (convert abstract meaning into text)
• Speech synthesis (convert text into speech)

Plato has been designed to be as modular and flexible as possible; it supportstraditional as well as custom conversational AI architectures, and importantly,enables multi-party interactions where multiple agents, potentially withdifferent roles, can interact with each other, train concurrently, and solvedistributed problems.

Figures 1 and 2, below, depict example Plato conversational agent architectureswhen interacting with human users and with simulated users, respectively. Interacting withsimulated usersis a common practice used in the research community tojump-start learning (i.e., learn some basic behaviours before interactingwith humans). Each individual component can be trained online or offlineusing any machine learning library (for instance, Ludwig,TensorFlow, PyTorch, oryour own implementations) as Plato is a universal framework. Ludwig, Uber's open source deeplearning toolbox, makes for a good choice, as it does not require writingcode and is fully compatible with Plato.

Figure 1: Plato's modular architecture means that any component can be trainedonline or offline and can be replaced by custom or pre-trained models.(Grayed components in this diagram are not core Plato components.)

Figure 2: Using a simulated user rather than a human user, as in Figure 1, wecan pre-train statistical models for Plato's various components. These can thenbe used to create a prototype conversational agent that can interact with humanusers to collect more natural data that can be subsequently used to trainbetter statistical models. (Grayed components in this diagram are not Platocore components since they are either available as out of the box such as GoogleASR and Amazon Lex, or domain and application specific such as custom databases/APIs.)

In addition to single-agent interactions, Plato supports multi-agentconversations where multiple Plato agents can interact with and learn from eachother. Specifically, Plato will spawn the conversational agents, make sure thatinputs and outputs (what each agent hears and says) are passed to each agentappropriately, and keep track of the conversation.

This setup can facilitate research in multi-agent learning, where agents needto learn how to generate language in order to perform a task, as well asresearch in sub-fields of multi-party interactions (dialogue state tracking,turn taking, etc.). The dialogue principles define what each agent canunderstand (an ontology of entities or meanings; for example: price, location,preferences, cuisine types, etc.) and what it can do (ask for more information,provide some information, call an API, etc.). The agents can communicate overspeech, text, or structured information (dialogue acts) and each agent has itsown configuration. Figure 3, below, depicts this architecture, outlining thecommunication between two agents and the various components:

Figure 3: Plato's architecture allows concurrent training of multiple agents,each with potentially different roles and objectives, and can facilitateresearch in fields such as multi-party interactions and multi-agent learning.(Grayed components in this diagram are not core Plato components.)

Finally, Plato supports custom architectures (e.g. splitting NLU into multipleindependent components) and jointly-trained components (e.g. text-to-dialoguestate, text-to-text, or any other combination) via the generic agentarchitecture shown in Figure 4, below. This mode moves away from the standardconversational agent architecture and supports any kind of architecture(e.g., with joint components, text-to-text or speech-to-speech components, orany other set-up) and allows loading existing or pre-trained models into Plato.

Figure 4: Plato's generic agent architecture supports a wide range ofcustomization, including joint components, speech-to-speech components, andtext-to-text components, all of which can be executed serially or in parallel.

Users can define their own architecture and/or plug their own components intoPlato by simply providing a Python class name and package path to that module,as well as the model’s initialization arguments. All the user needs to do islist the modules in the order they should be executed and Plato takes care ofthe rest, including wrapping the input/output, chaining the modules, andhandling the dialogues. Plato supports serial and parallel execution ofmodules.

Plato also provides support for Bayesian optimization of conversational AIarchitectures or individual module parameters through Bayesian Optimisation ofCombinatorial Structures (BOCS).

Installation

First make sure you have python version 3.6 or higher installed on your machine. Nextyou need to clone the Plato repository:

git clone [email protected]:uber-research/plato-research-dialogue-system.git

Next you need to install some pre-requisites:

• TensorFlow:

  pip install tensorflow>=1.14.0

• Install SpeechRecognition library for audio support:

  pip install SpeechRecognition

• For MacOS:

  brew install portaudiobrew install gmp pip install pyaudio

• For Ubuntu/Debian:

  sudo apt-get install python3-pyaudio

• For Windows: Nothing is needed to be pre-installed

Next step is installing Plato. To install Plato you should directly install it fromsource code.

Installing Plato from source code (Recommended)

Installing Plato from source code allows installation in editable mode which means thatif you make changes to the source code, it will directly effect execution.

1. Navigate to the directory of Plato (where you cloned the plato repository in the previousstep).

2. We recommend to create a new python environment. To set up the new python environment:

   2.1 install virtualenv

   sudo pip install virtualenv

   2.2 create a new python environment:

   python3 -m venv </path/to/new/virtual/environment>

   2.3 activate the new python environment:

   source </path/to/new/virtual/environment/bin>/bin/activate

3. Install Plato:

   pip install -e . 

To support speech it is necessary to install PyAudio,which has a number of dependencies that might not exist on a developer'smachine. If the steps above are unsuccessful, this poston a PyAudio installation error includes instructions on how to get thesedependencies and install PyAudio.

Common Issues During Installation

CommonIssues.md file contains common issues and their resolution that a usermight encounter while installation.

Running Plato

To run Plato after installation, you can simply run the plato command in the terminal.The plato command receives 4 sub-commands:

• run
• gui
• domain
• parse

Each of these sub-commands receives a value for the --config argument that points toa configuration file. We will describe these configuration files in detail later in thedocument but remember that plato run --config and plato gui --config receivean application configuration file(examples could be found here: example/config/application/),plato domain --config receives a domain configuration (examples could be found here:example/config/domain/), and plato parse --config receives a parser configurationfile (examples could be found here: example/config/parser/).

For the value that is passed to --config Plato first checks to see if the valueis the address of a file on the machine. If it is, then Plato tries to parse that file.If it is not, Plato checks to see if the value is a name of a file within theexample/config/<application, domain, or parser> directory.

For some quick examples, try the following configuration files for the CambridgeRestaurants domain:

plato run --config CamRest_user_simulator.yaml

plato run --config CamRest_text.yaml

plato run --config CamRest_speech.yaml

Building a Conversational Agent with Plato

An application, i.e. conversational system, in Plato contains three major parts:

• Domain
• Controller
• Agent

These parts are declared in an application configuration file. Examples ofsuch configuration files could be found at example/config/application/In the rest of this section we describe each of these parts in details.

Domain

For implementing a task-oriented dialog system in Plato the user needs to specify twocomponents that constitute the domain of the dialog system:

• Ontology
• Database

Plato provides a command for automating this process of building the ontology and database.Let's say for example that you want to build a conversational agent for aflower shop, and you have the following items in a .csv (this file could be found atexample/data/flowershop.csv):

id,type,color,price,occasion1,rose,red,cheap,any2,rose,white,cheap,anniversary3,rose,yellow,cheap,celebration4,lilly,white,moderate,any5,orchid,pink,expensive,any6,dahlia,blue,expensive,any

To automatically generate a .db SQL fileand a .json Ontology file you need to create a domain configuration file within whichyou should specify the path to the csv file, output paths, as well as informable,requestable, and system-requestable slots:(e.g. example/config/domain/create_flowershop_domain.yaml):

GENERAL:  csv_file_name: example/data/flowershop.csv  db_table_name: flowershop  db_file_path: example/domains/flowershop-dbase.db  ontology_file_path: example/domains/flowershop-rules.jsonONTOLOGY:  # Optional  informable_slots: [type, price, occasion]  requestable_slots: [price, color]  System_requestable_slots: [type, price, occasion]

and run the command:

plato domain --config create_flowershop_domain.yaml

If all went well, you should have a flowershop.json and aflowershop.db in the example/domains/ directory.

If you receive this error:

sqlite3.IntegrityError: UNIQUE constraint failed: flowershop.id

it means that the .db file has already been created.

You can now simply run Plato's dummy components as a sanity check and talk toyour flower shop agent:

plato run --config flowershop_text.yaml

Controller

Controllers are objects that orchestrate the conversation between theagents. The Controller will instantiate the agents, initialize them for eachdialogue, pass input and output appropriately, and keep track of statistics.

Running the command plato run runs Plato's basic controller(plato/controller/basic_controller.py). This command receives a value for the --configargument which points to a Plato application configuration file.

To run a Plato conversational agent, the user must run the following commandwith the appropriate configuration file:

plato run --config <FULL PATH TO CONFIG YAML FILE>

Please refer to example/config/application/ for example configuration files which containsettings on the environment and the agent(s) to be created as well as their components.The examples in example/config/application/ could be run directly using just the name ofthe example YAML file:

plato run --config <NAME OF A FILE FROM example/config/application/>

Alternatively a user could write their own config file and run Plato by passing thefull path to their config file to --config:

plato run --config <FULL PATH TO CONFIG YAML FILE>

For the value that is passed to --config plato first checks to see if the valueis the address of a file on the machine. If it is, the Plato tries to parse that file.If it is not, Plato checks to see if the value is a name of a file within theexample/config/application directory.

Agent

Each conversational AI application in Plato could have one or more agents. Each agenthas a role (system, user, ...) and a set of standard dialog system components(Figure 1), namely NLU, dialogue manager, dialogue state tracker, policy,NLG, and user simulator.

An agent could have one explicit module for each one of these components.Alternatively, some of these components could be combined into one or more modules(e.g. joint / end-to-end agents) that can run sequentially or in parallel (Figure 4).Plato's components are defined in plato.agent.component and all inherit fromplato.agent.component.conversational_module

Figure 5. Components of Plato agents

Note that any new implementations or custom modules should inherit fromplato.agent.component.conversational_module.

Each one of these modules could be either rule-based or trained. In thefollowing subsections we will describe how to build rule-based and trainedmodules for agents.

Rule-based modules

Plato provides rule-based versions of all components of a Slot-Fillingconversational agent (slot_filling_nlu, slot_filling_dst, slot_filling_policy,slot_filling_nlg, and the default version of agenda_based_us). These can beused for quick prototyping, baselines, or sanity checks.Specifically, all of these components follow rules or patterns conditionedon the given ontology and sometimes on the given database and should be treatedas the most basic version of what each component should do.

Trained modules

Plato supports training of agents’ components modules in an online(during the interaction) or offline (from data) manner, using any deep learningframework. Virtually any model can be loaded into Plato as long as Plato’sinterface Input/Output is respected. For example, if a model is a custom NLUmodule it simply needs to inherit from Plato's NLU abstract class(plato.agent.component.nlu) and implement the necessary abstract methods.

Plato internal experience

To facilitate online learning, debugging, and evaluation, Plato keeps track ofits internal experience in a structure called the Dialogue Episode Recorder,(plato.utilities.dialogue_episode_recorder)which contains information about previous dialogue states, actions taken,current dialogue states, utterances received and utterances produced, rewardsreceived, and a few other structs including a custom field that can be used totrack anything else that cannot be contained by the aforementioned categories.

At the end of a dialogue or at specified intervals, each conversational agentwill call the train() function of each of its internal components, passing thedialogue experience as training data. Each component then picks the parts itneeds for training.

To use learning algorithms that are implemented inside Plato, any externaldata, such as DSTC2 data, should be parsed into this Plato experience so thatthey may be loaded and used by the corresponding component under training.

Alternatively, users may parse the data and train their models outside of Platoand simply load the trained model when they want to use it for a Plato agent.

Parsing data with Plato

Training online is as easy as flipping the 'train' flags to 'True' in theconfiguration for each component users wish to train.

To train from data, users simply need to load the experience they parsed fromtheir dataset. Plato provides exampleparsers for DSTC2 andMetaLWOZ datasets. As anexample of how to use these parsers for offline training in Plato, we willuse the DSTC2 dataset, which can be obtained from the 2nd Dialogue StateTracking Challenge website:

http://camdial.org/~mh521/dstc/downloads/dstc2_traindev.tar.gz

Once the download is complete, you need to unzip the file. A config file forparsing this dataset is provided at example/config/parser/Parse_DSTC2.yaml.You can parse the data that you downloaded by first editingthe value of data_path in example/config/parser/Parse_DSTC2.yaml topoint to the path to where you downloaded and unzipped the DSTC2 data. Nextyou can run the parse script as follows:

plato parse --config Parse_DSTC2.yaml

Alternatively you could write your own config file and pass the absolute addressto that file to the command:

plato parse --config <absolute pass to parse config file>

Running this command will run the parsing script for DSTC2(which lives under plato/utilities/parser/parse_dstc2.py) andwill create the training data for dialog state tracker,NLU, and NLG for both user and system under the data directory at the rootdirectory of this repository. Now this parsed data could be used to trainmodels for different components of Plato.

Training Components of Conversational Agents

There are multiple ways to train each component of a Plato agent: online (asthe agent interacts with other agents, simulators, or users) or offline. Moreover,you can use algorithms implemented in Plato or you can use external frameworkssuch as TensorFlow, PyTorch, Keras, Ludwig, etc.

Ludwig is an open source deeplearning framework that allows you to train models without writing any code.You only need to parse your data into .csv files, create a ludwig config(in YAML), that describes the architecture you want, which features to use fromthe .csv and other parameters and then simply run a command in a terminal.

Ludwig also provides an API, that Plato is compatible with. This allows Platoto integrate with Ludwig models, i.e. load or save the models, train and querythem.

Train NLU

In the previous section, the DSTC2 parser of Plato generatedsome .csv files that can be used to train NLU and NLG. There is one NLU.csv file for the system (data/DSTC2_NLU_sys.csv) and one forthe user (data/DSTC2_NLU_usr.csv). These look like this:

For training a NLU model you need to write a configuration file that looks like this:

input_features:    -        name: transcript        type: sequence        reduce_output: null        encoder: parallel_cnnoutput_features:    -        name: intent        type: set        reduce_output: null    -        name: iob        type: sequence        decoder: tagger        dependencies: [intent]        reduce_output: nulltraining:    epochs: 100    early_stop: 50    learning_rate: 0.0025    dropout: 0.5    batch_size: 128

An example of this config file exists inexample/config/ludwig/ludwig_nlu_train.yaml. The training job could be startedby running:

ludwig experiment \       --model_definition_file example/config/ludwig/ludwig_nlu_train.yaml \       --data_csv data/DSTC2_NLU_sys.csv \       --output_directory models/camrest_nlu/sys/

The next step is to load the model in an application config. Inexample/config/application/CamRest_model_nlu.yaml we provide anapplication config that has a model based NLU and the other components arenon-ML-based. By updating the path to the mode (model_path) to the valuethat you provided to the --output_directory argument when you ran ludwig,you can specify the NLU model that the agent needs to use for NLU:

...MODULE_0:    package: applications.cambridge_restaurants.camrest_nlu      class: CamRestNLU      arguments:        model_path: <PATH_TO_YOUR_MODEL>/model...

and test that the model works:

plato run --config CamRest_model_nlu.yaml

Train DST

The DSTC2 Data Parser generated two .csv files we can use for DST:DST_sys.csv and DST_usr.csv which look like this:

Essentially, the parser keeps track of the previous dialogue state, the inputfrom NLU, and the resulting dialogue state. We can then feed this into Ludwigto train a Dialogue State Tracker. Here's the ludwig config which can also befound at example/config/ludwig/ludwig_dst_train.yaml:

input_features:    -        name: dst_prev_food        type: category    -        name: dst_prev_area        type: category    -        name: dst_prev_pricerange        type: category    -        name: dst_intent        type: category    -        name: dst_slot        type: category    -        name: dst_value        type: categoryoutput_features:    -        name: dst_food        type: category    -        name: dst_area        type: category    -        name: dst_pricerange        type: category    -        name: dst_req_slot        type: categorytraining:  epochs: 100

We now need to train our model with ludwig:

ludwig experiment \       --model_definition_file example/config/ludwig/ludwig_dst_train.yaml \       --data_csv data/DST_sys.csv \       --output_directory models/camrest_dst/sys/

and run a Plato agent with the model-based DST:

plato run --config CamRest_model_dst.yaml

You can of course experiment with other architectures and training parameters.

Train Dialogue Policy

So far we have seen how to train components of Plato agents using externalframeworks (i.e. Ludwig). In this section, we will see how to use Plato's internalalgorithms to train a dialogue policy offline, using supervised learning,and online, using reinforcement learning.

Supervised Dialogue Policy

Apart from the .csv files, the DSTC2 parser used Plato's dialogue episoderecorder to also save the parsed dialogues in Plato experience logs here:logs/DSTC2_system and logs/DSTC2_user. These logs contain information abouteach dialogue, for example current dialogue state, action taken, next dialoguestate, reward observed, input utterance, success, etc. These logs can be directlyloaded into a conversational agent and can be used to fill the experience pool.

All you need to do then is write a configuration file that loads these logs(example/config/CamRest_model_supervised_policy_train.yaml):

GENERAL:  ...  experience_logs:    save: False    load: True    path: logs/DSTC2_system  ...DIALOGUE:  # Since this configuration file trains a supervised policy from data loaded  # from the logs, we only really need one dialogue just to trigger the train.  num_dialogues: 1  initiative: system  domain: CamRestAGENT_0:  role: system  max_turns: 15  train_interval: 1  train_epochs: 100  save_interval: 1    ...  DM:    package: plato.agent.component.dialogue_manager.dialogue_manager_generic    class: DialogueManagerGeneric    arguments:      DST:        package: plato.agent.component.dialogue_state_tracker.slot_filling_dst        class: SlotFillingDST      policy:        package: plato.agent.component.dialogue_policy.deep_learning.supervised_policy        class: SupervisedPolicy        arguments:          train: True          learning_rate: 0.9          exploration_rate: 0.995          discount_factor: 0.95          learning_decay_rate: 0.95          exploration_decay_rate: 0.995          policy_path: models/camrest_policy/sys/sys_supervised_data            ...

Note that we only run this agent for one dialogue but train for 100 epochs,using the experience that is loaded from the logs:

plato run --config CamRest_model_supervised_policy_train.yaml

After training is complete, we can test our supervised policy:

plato run --config CamRest_model_supervised_policy_test.yaml

Reinforce Dialogue Policy

In the previous section, we saw how to train a Supervised dialogue policy. Wecan now see how we can train a Reinforcement Learning policy, using theReinforce algorithm. To do this, we define the relevant class in the configurationfile:

...AGENT_0:  role: system  max_turns: 15  train_interval: 500  train_epochs: 3  train_minibatch: 200  save_interval: 5000  ...  DM:    package: plato.agent.component.dialogue_manager.dialogue_manager_generic    class: DialogueManagerGeneric    arguments:      DST:        package: plato.agent.component.dialogue_state_tracker.slot_filling_dst        class: SlotFillingDST      policy:        package: plato.agent.component.dialogue_policy.deep_learning.reinforce_policy        class: ReinforcePolicy        arguments:          train: True          learning_rate: 0.9          exploration_rate: 0.995          discount_factor: 0.95          learning_decay_rate: 0.95          exploration_decay_rate: 0.995          policy_path: models/camrest_policy/sys/sys_reinforce            ...

Note the learning parameters under AGENT_0 and the algorithmic-specificparameters under the policy's arguments. We then call plato with this configuration:

plato run --config CamRest_model_reinforce_policy_train.yaml

and test the trained policy model:

plato run --config CamRest_model_reinforce_policy_test.yaml

Note that other components can also be trained online, either using Ludwig'sAPI or by implementing the learning algorithms in Plato.

Note also that log files can be loaded and used as experience pool for anycomponent and learning algorithm. However, you may need to implement your ownlearning algorithms for some Plato components.

Train NLG

To train an NLG module you need to write aconfiguration file that looks like this (e.g.example/config/application/CamRest_model_nlg.yaml):

---input_features:    -        name: nlg_input        type: sequence        encoder: rnn        cell_type: lstm        output_features:    -        name: nlg_output        type: sequence        decoder: generator        cell_type: lstmtraining:    epochs: 20    learning_rate: 0.001    dropout: 0.2

and train your model:

ludwig experiment \       --model_definition_file example/config/ludwig/ludwig_nlg_train.yaml \       --data_csv data/DSTC2_NLG_sys.csv \       --output_directory models/camrest_nlg/sys/

The next step is to load the model in Plato. Go to theCamRest_model_nlg.yaml configuration file and update the path ifnecessary:

...  NLG:    package: applications.cambridge_restaurants.camrest_nlg    class: CamRestNLG    arguments:      model_path: models/camrest_nlg/sys/experiment_run/model...

and test that the model works:

plato run --config CamRest_model_nlg.yaml

Remember that Ludwig will create a new experiment_run_i directory each timeit is called, so please make sure you keep the correct path in Plato's configup to date.

Note that Ludwig also offers a method to train your model online, so inpractice you need to write very little code to build, train, and evaluate anew deep learning component in Plato.

Train an end-to-end conversational agent based on Seq2Seq

For this example, we will use the MetalWoz dataset that you can download fromhere.

Plato supports jointly trained models through Generic Agents. Here we will seethe steps needed to create a simple seq2seq conversational agent from scratch.Using MetalWOZ as an example, we need to do the following:

1. Write a MetalWOZ data parser that reads the data and procudes CSV files

As we are only training a simple seq2seq model (text to text), we need ourparser to extract user and system utterances. These will be saved in .csv filesthat will be used by Ludwig in step 4.

For a simple implementation of a MetalWOZ parser, seeutilities/parser/Parse_MetalWOZ.py

Please note that this parser will only parse one single file (one domain). Youcan easily modify it, however, to fit your needs. Here is a sample of theoutput produced by the parser for the pizza ordering domain:

Note the first user utterance does not actually exist in the data. However,we need something to prompt the model to produce the system's greeting - wecould have used an empty sentence, or any other greeting (or a combination ofthese).

2. Write a config for plato parse

You can then run plato parse as follows:

plato parse --config Parse_MetalWOZ.yaml

3. Train an end-to-end model

To get started we can train a very simple model using Ludwig (feel free to useyour favourite deep learning framework here):

input_features:    -        name: user        type: text        level: word        encoder: rnn        cell_type: lstm        reduce_output: nulloutput_features:    -        name: system        type: text        level: word        decoder: generator        cell_type: lstm        attention: bahdanautraining:  epochs: 100

You can modify this config to reflect the architecture of your choice and trainusing Ludwig:

ludwig train \       --data_csv data/metalwoz.csv \       --model_definition_file example/config/ludwig/metalWOZ_seq2seq_ludwig.yaml \       --output_directory "models/joint_models/"

4. Write a class inheriting from Conversational Module that loads and queries the model

This class simply needs to handle loading of the model, querying itappropriately and formatting its output appropriately. In our case, we need towrap the input text into a pandas dataframe, grab the predicted tokens fromthe output and join them in a string that will be returned. See the class here:plato.agent.component.joint_model.metal_woz_seq2seq.py

5. Write a Plato generic yaml config and run your agent!

See example/config/application/metalwoz_generic.yaml for an example genericconfiguration file that interacts with the seq2seq agent over text. You can tryit out as follows:

plato run --config metalwoz_text.yaml

Remember to update the path to your trained model if necessary! The defaultpath assumes you run the ludwig train command from Plato's root directory.

Training Multiple Conversational Agents Simultaneously

One of Plato's main features allows two agents to interact with each other.Each agent can have a different role (for instance, system and user), differentobjectives, and receive different reward signals. If the agents arecooperating, some of these can be shared (e.g., what constitutes a successfuldialogue).

To run multiple Plato agents on the Cambridge Restaurants domain, we run thefollowing commands to train the agents’ dialogue policies and test them:

• Training phase: 2 policies (1 for each agent) are trained.These policies are trained using the WoLFalgorithm:

  plato run --config MultiAgent_train.yaml

• Testing phase: uses the policy trained in the training phase to createdialogs between two agents:

  plato run --config MultiAgent_test.yaml

While the basic controller currently allows two agent interaction, it is fairlystraightforward to extend it to multiple agents (e.g. with a blackboard architecture,where each agent broadcasts its output to other agents). This can support scenariossuch as smart homes, where every device is an agent, multi-user interactions withvarious roles, and more.

User Simulators

Plato provides implementations for two kinds of user simulators. One is the verywell known Agenda-Based User Simulator, and the other is a simulator thatattempts to mimic user behaviour observed in data. However, we encourageresearchers to simply train two conversational agents with Plato (one being a'system' and one being a 'user') instead of using simulated users, when possible.

Agenda-Based User Simulator

The Agenda-Based User Simulator was proposed by Schatzmann and is explainedin detail in thispaper.Conceptually, the simulator maintains an "agenda"of things to say, which is usually implemented as a stack. When the simulatorreceives input, it consults its policy (or its set of rules) to see what contentto push into the agenda, as a response to the input. After some housekeeping(e.g. removing duplicates or content that is no longer valid), the simulatorwill pop one or more items off the agenda that will be used to formulate itsresponse.

The Agenda-Based User Simulator also has an error simulation module, that cansimulate speech recognition / language understanding errors. Based on someprobabilities, it will distort the output dialogue acts of the simulator - theintent, slot, or value (different probability for each). Here is an exampleof the full list of parameters that this simulator receives:

patience: 5                         # Stop after <patience> consecutive identical inputs receivedpop_distribution: [0.8, 0.15, 0.05] # pop 1 act with 0.8 probability, 2 acts with 0.15, etc.slot_confuse_prob: 0.05             # probability by which the error model will alter the output dact slot op_confuse_prob: 0.01               # probability by which the error model will alter the output dact operatorvalue_confuse_prob: 0.01            # probability by which the error model will alter the output dact valuenlu: slot_filling                   # type of NLU the simulator will usenlg: slot_filling                   # type of NLG the simulator will use

Dialogue Act to Language User Simulator

This simulator was designed to be a simple policy-based simulator, that canoperate at the dialogue act level or at the utterance level. To demonstratehow it works, the DSTC2 parser created a policy file for this simulator:user_policy_reactive.pkl (reactive because it reacts to system dialogueacts instead of user simulator state). This is actually a simple dictionary of:

System Dial. Act 1 -->  {'dacts': {User Dial. Act 1: probability}                                  {User Dial. Act 2: probability}                                  ...                                                           'responses': {User utterance 1: probability}                                      {User utterance 2: probability}                                      ...                                      System Dial. Act 2 --> ...

The key represents the input dialogue act (e.g. coming from the systemconversational agent). The value of each key is a dictionary of two elements,representing probability distributions over dialogue acts or utterance templatesthat the simulator will sample from.

To see an example, you can run the following configuration:

plato run --config CamRest_dtl_simulator.yaml

Create a new module

There are two ways to create a new module depending on its function. If amodule, for example, implements a new way of performing NLU or dialogue policy,then you should write a class that inherits from the corresponding abstract class.

If, however, a module does not fit one of the single agent basic components,for example, it performs Named Entity Recognition or predicts dialogue actsfrom text, then you must write a class that inherits from theconversational_module directly. You can then load the module via ageneric agent by providing the appropriate package path, class name, andarguments in the configuration.

...MODULE_i:    package: my_package.my_module    Class: MyModule    arguments:      model_path: models/my_module/parameters/      ......

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