Aleksandr Nisnevich

TL;DR: It is posited that the present success of representation learning approaches trained on large text corpora can be deeply enriched from the parallel tradition of research on the contextual and social nature of language.

TL;DR: An approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph, which enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict.

TL;DR: The authors posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

TL;DR: In this article, the authors describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph and a dialogue agent maps each user utterance to a program that extends this graph.

TL;DR: This paper considers how to build an AI-powered human-in-the-loop process to enable efficient data collection, and investigates how to best support users in efficiently traversing the candidate set and locating the correct parse, in terms of speed and accuracy.