Can Deep Learning Compensate for a Shallow Evaluation?

Gerald Penn

Abstract: The last ten years have witnessed an enormous increase in the application of "deep learning" methods to both spoken and textual natural language processing. Have they helped? With respect to some well-defined tasks such as language modelling and acoustic modelling, the answer is most certainly affirmative, but those are mere components of the real applications that are driving the increasing interest in our field. In many of these real applications, the answer is surprisingly that we cannot be certain because of the shambolic evaluation standards that have been commonplace --- long before the deep learning renaissance --- in the communities that specialized in advancing them.This talk will consider three examples in detail: sentiment analysis, text-to-speech synthesis, and summarization. We will discuss empirical grounding, the use of inferential statistics alongside the usual, more engineering-oriented pattern recognition techniques, and the use of machine learning in the process of conducting an evaluation itself.