A Neural Conversational Model
Venue
ICML Deep Learning Workshop (2015)
Publication Year
2015
Authors
BibTeX
Abstract
Conversational modeling is an important task in natural language understanding and
machine intelligence. Although previous approaches exist, they are often restricted
to specific domains (e.g., booking an airline ticket) and require hand-crafted
rules. In this paper, we present a simple approach for this task which uses the
recently proposed sequence to sequence framework. Our model converses by predicting
the next sentence given the previous sentence or sentences in a conversation. The
strength of our model is that it can be trained end-to-end and thus requires much
fewer hand-crafted rules. We find that this straightforward model can generate
simple conversations given a large conversational training dataset. Our preliminary
results suggest that, despite optimizing the wrong objective function, the model is
able to converse well. It is able extract knowledge from both a domain specific
dataset, and from a large, noisy, and general domain dataset of movie subtitles. On
a domain-specific IT helpdesk dataset, the model can find a solution to a technical
problem via conversations. On a noisy open-domain movie transcript dataset, the
model can perform simple forms of common sense reasoning. As expected, we also find
that the lack of consistency is a common failure mode of our model.
