snips-nlu 0.20.1

Summary

• What is Snips NLU about ?

• Getting Started

  • System requirements

  • Installation

  • Language Resources

• API Usage

  • Sample code

  • Command Line Interface

• Sample datasets

• Benchmarks

• Documentation

• Citing Snips NLU

• FAQ & Community

• Related content

• How do I contribute ?

• Licence

What is Snips NLU about ?

Behind every chatbot and voice assistant lies a common piece of technology: Natural Language Understanding (NLU). Anytime a user interacts with an AI using natural language, their words need to be translated into a machine-readable description of what they meant.

The NLU engine first detects what the intention of the user is (a.k.a. intent), then extracts the parameters (called slots) of the query. The developer can then use this to determine the appropriate action or response.

Let’s take an example to illustrate this, and consider the following sentence:

"What will be the weather in paris at 9pm?"

Properly trained, the Snips NLU engine will be able to extract structured data such as:

{
   "intent": {
      "intentName": "searchWeatherForecast",
      "probability": 0.95
   },
   "slots": [
      {
         "value": "paris",
         "entity": "locality",
         "slotName": "forecast_locality"
      },
      {
         "value": {
            "kind": "InstantTime",
            "value": "2018-02-08 20:00:00 +00:00"
         },
         "entity": "snips/datetime",
         "slotName": "forecast_start_datetime"
      }
   ]
}

In this case, the identified intent is searchWeatherForecast and two slots were extracted, a locality and a datetime. As you can see, Snips NLU does an extra step on top of extracting entities: it resolves them. The extracted datetime value has indeed been converted into a handy ISO format.

Check out our blog post to get more details about why we built Snips NLU and how it works under the hood. We also published a paper on arxiv, presenting the machine learning architecture of the Snips Voice Platform.

Getting Started

pip install snips-nlu

python -m snips_nlu download en

snips-nlu download en

API Usage

snips-nlu train path/to/dataset.json path/to/output_trained_engine

snips-nlu parse path/to/trained_engine

>>> from __future__ import unicode_literals, print_function
>>> import io
>>> import json
>>> from snips_nlu import SnipsNLUEngine
>>> from snips_nlu.default_configs import CONFIG_EN
>>> with io.open("sample_datasets/lights_dataset.json") as f:
...     sample_dataset = json.load(f)
>>> nlu_engine = SnipsNLUEngine(config=CONFIG_EN)
>>> nlu_engine = nlu_engine.fit(sample_dataset)
>>> text = "Please turn the light on in the kitchen"
>>> parsing = nlu_engine.parse(text)
>>> parsing["intent"]["intentName"]
'turnLightOn'

Sample datasets

Here is a list of some datasets that can be used to train a Snips NLU engine:

• Lights dataset: “Turn on the lights in the kitchen”, “Set the light to red in the bedroom”

• Beverage dataset: “Prepare two cups of cappucino”, “Make me a cup of tea”

• Flights dataset: “Book me a flight to go to boston this weekend”, “book me some tickets from istanbul to moscow in three days”

Benchmarks

In January 2018, we reproduced an academic benchmark which was published during the summer 2017. In this article, authors assessed the performance of API.ai (now Dialogflow, Google), Luis.ai (Microsoft), IBM Watson, and Rasa NLU. For fairness, we used an updated version of Rasa NLU and compared it to the latest version of Snips NLU (both in dark blue).

In the figure above, F1 scores of both intent classification and slot filling were computed for several NLU providers, and averaged accross the three datasets used in the academic benchmark mentionned before. All the underlying results can be found here.

Documentation

To find out how to use Snips NLU please refer to the package documentation, it will provide you with a step-by-step guide on how to setup and use this library.

Citing Snips NLU

Please cite the following paper when using Snips NLU:

@article{coucke2018snips,
  title   = {Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces},
  author  = {Coucke, Alice and Saade, Alaa and Ball, Adrien and Bluche, Th{\'e}odore and Caulier, Alexandre and Leroy, David and Doumouro, Cl{\'e}ment and Gisselbrecht, Thibault and Caltagirone, Francesco and Lavril, Thibaut and others},
  journal = {arXiv preprint arXiv:1805.10190},
  pages   = {12--16},
  year    = {2018}
}

FAQ & Community

Please join the forum to ask your questions and get feedback from the community.

Related content

• What is Snips about ?

• Snips NLU Open sourcing blog post

• Snips Voice Platform paper (arxiv)

• Snips NLU Language Resources

• Bug tracker

• Snips NLU Rust: Rust inference pipeline implementation and bindings (C, Swift, Kotlin, Python)

• Rustling: Snips NLU builtin entities parser

How do I contribute ?

Please see the Contribution Guidelines.

Licence

This library is provided by Snips as Open Source software. See LICENSE for more information.

Geonames Licence

The snips/city, snips/country and snips/region builtin entities rely on software from Geonames, which is made available under a Creative Commons Attribution 4.0 license international. For the license and warranties for Geonames please refer to: https://creativecommons.org/licenses/by/4.0/legalcode.