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Facebook Open-Sources PyText for Faster Natural Language Processing Development


In a recent blog post, Facebook announced they have open-sourced PyText, a modeling framework, used in natural language processing (NLP) systems. PyText is a library built upon PyTorch and improves the effectiveness of promoting experimentation projects to large-scale production deployments.

PyText offers several advantages when building NLP applications over traditional approaches. Ahmed Aly Hegazy, a software engineer at Facebook, provided the following benefits:

PyText provides a simplified workflow which enables faster experimentation. This occurs through access to a rich set of prebuilt model architectures and utilities for text processing and vocabulary management to facilitate large-scale deployment. In addition, the ability to harness the PyTorch ecosystem, including prebuilt models and tools created by researchers and engineers in the NLP community also provides opportunities.

NLP systems facilitate human to computer interactions through natural language and the use of artificial intelligence. Facebook leverages natural language processing in many of their services including Messenger and their video conferencing device called Portal.

Facebook has been able to reduce time to market for many of their own features, as a result of using PyText, Aly Hegazy explains:

At Facebook, we’ve used this framework to take NLP models from idea to full implementation in just days, instead of weeks or months, and to deploy complex models that rely on multitask learning.

Historically, natural language processing frameworks have struggled addressing the needs of both research, or experimentation, projects and production workloads. Aly Hegazy attributes many of the challenges associated to:

NLP systems can require creating, training, and testing dozens of models, and which use an inherently dynamic structure. Research-oriented frameworks can provide an easy, eager-execution interface that speeds the process of writing advanced and dynamic models, but they also suffer from increased latency and memory use in production.

PyText is able to avoid many of these research project-to-production challenges as it is built on top of PyTorch 1.0 which accelerates the path from research to production. Facebook has been able to demonstrate PyText’s capabilities at scale as it is used in more than a billion daily predictions while meeting Facebook’s stringent latency requirements. An example of how Facebook uses PyText is in Portal as it is used to interpret commands from users. When a user instructs Portal to “call my dad”, PyText is able to use semantic parsing to understand the relationship between the user and the person they are trying to call.

PyText complements Facebook’s other NLP investments, including integration with fastText, a text classification library. It is also able to improve DeepText, a text understanding engine, by providing semantic parsing and implementing multitask learning models that cannot be built using DeepText.

The benefits that Facebook has accrued by using PyText go beyond time-to-market efficiencies and extend to improved accuracy. Aly Hegazy explains:

We have used PyText to iterate quickly on incremental improvements to Portal’s NLP models, such as ensembling, conditional random fields, and a conflated model for all domains. This has improved the accuracy of our models for our core domains by 5 percent to 10 percent. We also used PyText’s support for distributed training to reduce training time by 3-5x for Portal.

The PyText framework is available on GitHub and includes pretrained models and tutorials for training and deploying PyText models at scale.

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