To learn about the technologies and resources used, refer to the Liberty Mutual LUIS documentation.

• ✔️ Retrieved Utterances from Power BI

• ✔️ Added utterances to batch files (600)

• ✔️ Created pipeline on adding utterances through Excel

• ✔️ Created JavaScript script that adds and trains new utterances into model

• ✔️ Created pipeline for adding entities and its sub-entities to utterances through Excel

• ❌ Create different models with different intents to test

• ✔️ Framework for testing and analyzing/improving prediction score and/or accuracy

• ✔️ Framework for training completed data using JS script

Main framework:

Testing team will:

• Test random batch files with utterances to see how model behaves.
• Analyze results and pinpoint bottlenecks in data (i.e. wrong labeled entity, low prediction score, etc.).
• Communicate those results with training team to improve training data.
• Repeat steps 1 - 3 with different batch files and different models.

Training team will:

• Use Excel pipeline to structure JSON files with correct utterances and its respective entities/sub-entities.
• Target specific utterances and entities based on model behavior.
• Use JavaScript automation script to send and train the data into LUIS endpoint.
• Repeat steps 1 - 3 with different training sets and different models.

Uses the request API to grab utterances and send to LUIS bot for training.

Uses POST and GET methods to supply training data into the LUIS endpoint.

The train function performs the training process asynchronously for a smooth process.

const train = async (verb) => {

    const options = {
        uri: addTrainURI,
        method: verb,
        headers: {
            'Ocp-Apim-Subscription-Key': LUIS_authoringKey
        },
        json: true,
        body: null // The body can be empty for a training request
    };

    const response = await request(options)
    console.log("train " + verb + ":\n" + JSON.stringify(response, null, 2));
}

To use the transferDataScript.js file, make sure to connect to the node environment using

npm init

or

copying C:\Windows\System32\cmd.exe /k "C:\Program Files\nodejs\nodevars.bat" into the command prompt.

• Add the utterance data into the utterances variable.
• Adjust all endpoint URLS --> LUIS_appId, LUIS_authoringKey, LUIS_endpoint, and LUIS_versionId to connect to the correct bot.

Save the file and on the CLI, run node transferDataScript.js and expect to see "done" OR a 400 error.

Alternative method of sending/training utterance data TBA

In simple terms, an f-score is a measure of a test's accuracy. According to wikipedia.com, it is calculated from the precision and recall of the test.

Precision is:

(number of true positives) / (number of true AND false positives)

Recall is:

(number of true positives) / (number of all samples that SHOULD have been identified as positive)

The f-score has helped us gauge at the digital assistant's intelligence. It gives us a rough number of how many utterances it's correctly labeling.

In our case, we calculated the average f-scores for all of the performances in each intent.

fScore_Avg = (total f-score for each intent) / (all 11 intents)

[insert f-score average graph here]

[insert correct/incorrect/unclear prediction graph here per version]