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RIVA Intent Slot

Logo for RIVA Intent Slot
Intent and Slot classification of the qeuries for the weather chat bot (trained on weather chat bot data).
Latest Version
April 4, 2023
422.76 MB

IntentAndSlotClassification Model Card

Model Overview

Joint Intent classification and Slot classification is a task of classifying an Intent and detecting all relevant Slots (Entities) for this Intent in a query. For example, in the query: What is the weather in Santa Clara tomorrow morning?, we would like to classify the query as a Weather Intent, and detect Santa Clara as a Location slot and tomorrow morning as a date_time slot.

Intended Use

Intents and Slots names are usually task specific and defined as labels in the training data. This is a fundamental step that is executed in any task-driven Conversational Assistant. The primary use case of this model is to jointly identify Intents and Entities in a given user query.

Model Architecture

This is a pretrained Bert based model with 2 linear classifier heads on the top of it, one for classifying an intent of the query and another for classifying slots for each token of the query. This model is trained with the combined loss function on the Intent and Slot classification task on the given dataset.

For each query the model will classify it as one the intents from the intent dictionary and for each word of the query it will classify it as one of the slots from the slot dictionary, including out of scope slot for all the remaining words in the query which does not fall in another slot category. Out of scope slot (O) is a part of slot dictionary that the model is trained on.

Training Data

We used a proprietary data set that was collected via Mechanical Turk to describe different queries in weather domain.

List of the recognized Intents for this model:

  • weather
  • temperature, Temperature_yes_no
  • rainfall, rainfall_yes_no
  • snow, snow_yes_no
  • humidity, humidity_yes_no
  • windspeed
  • sunny
  • cloudy
  • context

List of the recognized Entities:

  • O (out of scope)
  • weathertime
  • weatherplace
  • temperatureunit
  • current_location
  • wind_speed_unit
  • rainfallunit
  • snowunit
  • alert_type
  • weatherforecastdaily


Training dataset included 9500 queries related to the weather topic and about 100K total words in the queries (slots) and 2000 queries for testing. The model was trained for 30 epochs after which it stopped giving improvements. We got around 95% intent accuracy and 93% slot accuracy for the given dataset.

How to Use This Model

These model checkpoints are intended to be used with Nvidia Riva.



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Suggested reading

  • Deploy your model for Production using Riva. Learn more about Riva framework

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