Towards meaningful, grounded conversations with intelligent agents

TL;DR

This paper advocates for a paradigm shift in conversational AI towards more meaningful, grounded interactions by emphasizing the importance of handling concrete and abstract entities and evolving relations for long-term, realistic conversations.

Contribution

It proposes a new conversation paradigm that moves beyond slot filling, focusing on models capable of managing complex entities and relations for more realistic interactions.

Findings

Highlights limitations of current slot filling approaches

Emphasizes need for models handling concrete and abstract entities

Suggests importance of evolving relations in conversations

Abstract

As conversational agents become integral parts of many aspects of our lives, current approaches are reaching bottlenecks of performance that require increasing amounts of data or increasingly powerful models. It is also becoming clear that such agents are here to stay and accompany us for long periods of time. If we are, therefore, to design agents that can deeply understand our world and evolve with it, we need to take a step back and revisit the trade-offs we have made in the current state of the art models. This paper argues that a) we need to shift from slot filling into a more realistic conversation paradigm; and b) that, to realize that paradigm, we need models that are able to handle concrete and abstract entities as well as evolving relations between them.