off-the-shelf training data sets

Blog Home / AI & Machine Learning   •   January 23, 2020

New from Appen: Off-the-shelf Training Data Sets

How Off-the-shelf Training Data Sets Can Save Your ML Teams Time and Money

Natural language processing (NLP) has become a crucial technology for driving many AI-based innovations. For sentiment analysis, development of virtual assistants, and many other applications, effective use of NLP can mean the difference between creating a successful product that performs accurately and one that does not. As AI Business explains, “NLP is leveraged within almost every text analytics solution. It’s the cognitive computing component focused on linguistics and language’s classification.”

But a successful NLP project needs training data – and a lot of it. Creating a high-quality dataset with the right degree of accuracy for training machine learning (ML) algorithms can be a difficult uplift for getting AI and ML projects off the ground. Not every company has a specialized team of ML PhDs, data engineers, and human annotators at their disposal. This is largely due to the expense of such a team. Instead, machine learning teams are turning to bespoke, off-the-shelf training data sets. These off-the-shelf training data sets offer a cost-effective alternative, especially those that are high-quality and customized for specific project types.

Finding data sets that have high accuracy labels can also be a difficult task. Many data sets out there may be old, uncleaned, or irrelevant. To help companies get their ML initiatives off the ground, Appen has made its entire catalog of Natural Language Processing data sets available from its website. Users are now able to browse diverse NLP data sets and request quotes for one or multiple data sets including:

  • Fully transcribed speech data sets for broadcast, call center, in-car, and telephony applications
  • Pronunciation lexicons, including both general and domain-specific (e.g. names, places, natural numbers)
  • Part-of-speech-tagged lexicons and thesauri
  • Text corpora notated for morphological information and named entities.

Machine Learning Projects that Benefit from Off-the-shelf Training Data Sets

bespoke training data sets

Cataloged by regional dialect and speaking style, Appen’s collection of over 230 high-quality data sets offers essential tools for companies to tap, including customizing AI offerings such as automatic speech recognition (ASR), text-to-speech (TTS), and more for their target markets. AI applications based on natural language processing (NLP) and conversational understanding require a high level of linguistic expertise in their development phase. Yet, this shouldn’t be overlooked as high-quality data sets that have been annotated with NLP in mind removes significant burdens for teams developing these projects. Typical use cases for Appen’s resource-saving natural language data sets include automatic speech recognition, TTS projects, and machine translation.

Automatic Speech Recognition (ASR)

Accurate automatic speech recognition (ASR) systems are crucial for improving communication and convenience across a wide range of applications — from video and photo captioning, to identifying questionable content, to building more helpful AI assistive technologies. But, as we’ve mentioned, building highly accurate speech recognition models usually requires vast amounts of computing and annotation resources. The plot thickens when you consider not only the staggering number of languages around the globe, but also dialects within those languages.

Text to Speech (TTS)

Similar challenges exist for TTS projects. This assistive technology can be highly effective for applications such as mobile phones, in-car systems, consumer medicine, and virtual assistants. These technologies all depend on TTS systems to function, and those systems need to be accurately trained with high-quality speech data to ensure accurate responses.

Machine Translation

Automatic translation, if highly accurate, can mean the difference between a good and bad customer experience. Building your machine translation engine with high-quality training data is crucial to achieving the kind of accuracy that users find helpful rather than frustrating. As you may have guessed, the key to creating a coherent and useful translation engine requires massive amounts of expertly annotated language data.

These are just a few examples of projects that can benefit from Appen’s off-the-shelf natural language data sets. Because the obstacles of time and money involved in creating data sets of your own have been removed, you can bring your natural language product to market faster and with confidence that your ML model has been trained with the highest level of quality available.