Deploying Lifelong Open-Domain Dialogue Learning

08/18/2020
by   Kurt Shuster, et al.
0

Much of NLP research has focused on crowdsourced static datasets and the supervised learning paradigm of training once and then evaluating test performance. As argued in de Vries et al. (2020), crowdsourced data has the issues of lack of naturalness and relevance to real-world use cases, while the static dataset paradigm does not allow for a model to learn from its experiences of using language (Silver et al., 2013). In contrast, one might hope for machine learning systems that become more useful as they interact with people. In this work, we build and deploy a role-playing game, whereby human players converse with learning agents situated in an open-domain fantasy world. We show that by training models on the conversations they have with humans in the game the models progressively improve, as measured by automatic metrics and online engagement scores. This learning is shown to be more efficient than crowdsourced data when applied to conversations with real users, as well as being far cheaper to collect.

READ FULL TEXT

page 3

page 14

11/02/2018

Engaging Image Chat: Modeling Personality in Grounded Dialogue

To achieve the long-term goal of machines being able to engage humans in...
10/21/2021

Modeling Performance in Open-Domain Dialogue with PARADISE

There has recently been an explosion of work on spoken dialogue systems,...
03/24/2022

Language Models that Seek for Knowledge: Modular Search Generation for Dialogue and Prompt Completion

Language models (LMs) have recently been shown to generate more factual ...
11/07/2021

A Word on Machine Ethics: A Response to Jiang et al. (2021)

Ethics is one of the longest standing intellectual endeavors of humanity...
07/25/2022

A Multi-Party Dialogue Ressource in French

We present Dialogues in Games (DinG), a corpus of manual transcriptions ...
01/12/2022

Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents

At the heart of improving conversational AI is the open problem of how t...
11/21/2015

Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

A long-term goal of machine learning is to build intelligent conversatio...