Amazon Alexa has tens of thousands of voice apps — skills, in Amazon’s vernacular — contributed by third-party developers.
Usually, building said skills requires supplying examples of customer requests (e.g., “Order my usual”) together with the actions to which those requests should map, which are used to train the AI system that processes real requests in production.
Needless to say, that’s labor-intensive, which is why scientists at Amazon are exploring techniques for pooling sample requests for similar requests from different skills at training time.
In a paper presented last week at the Association for Computational Linguistics conference in Florence, the coauthors write that the additional data improved performance by “plugging holes” in lists of example requests.
Evaluated on two different public corpora and an internal corpus, they report that training an AI system simultaneously on multiple skills yielded better results than training it separately for each skill.
As the researchers note, multitask training runs the risk of causing a model to lose focus on task-specific structures.