Tuesday, June 26, 2018

Salesforce Research creates Swiss Army Knife for natural language processing

Salesforce has created a multi-task model for natural language processing that takes an architecture approach.


Salesforce Research has created a natural language processing architecture that can handle multiple models and tasks. Typically, natural language processing (NLP) has a model for each function such as translation, sentiment analysis and question and answer.

The research, led by Salesforce Chief Scientist Richard Socher, revolves around a challenge dubbed Natural Language Decathlon (decaNLP). The challenge spans 10 tasks--question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, relation extraction, goal-oriented dialogue, database query generation, and pronoun resolution--and feeds into a system that jointly learns.

Think of decaNLP as a Swiss Army Knife for natural language processing. If NLP is customized repeatedly it won't scale. Salesforce was looking for a general purpose NLP approach where every task is transformed into a question answering format and trained jointly.

Socher said the approach melds deep learning and NLP and moves the discussion to one that revolves around a meta-architecture. He added that an architecture approach can also be used to prevent model sprawl as NLP functions are layered together.

"This is a project that can have immediate useful applications because it's a model that is a single deploy and easier to maintain," said Socher. "We're bringing a bunch of tools together."

Salesforce is likely to use the decaNLP approach in its product roadmap for Einstein and its various clouds.

The decaNLP is combined with a multitask question answering network that jointly learns all tasks without any specific model. The network also allows for adaptation by completing new tasks through related descriptions.

Here's a diagram of the multitask question answering network.


And finally, Salesforce Research came up with code for processing data sets, training and evaluating models and ultimately coming up with a score called the decay score.

NLP trained on the decaNLP system will, in theory, be better equipped to provide a framework for chatbots as well as any information in a customer service exchange.


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