One Time of Interaction May Not Be Enough: Go Deep with an
Interaction-over-Interaction Network for Response Selection in Dialogues
Abstract
Currently, researchers have paid great attention to retrieval-based dialogues in opendomain. In particular, people study the problem by investigating context-response matching for multi-turn response selection based
on publicly recognized benchmark data sets.
State-of-the-art methods require a response
to interact with each utterance in a context
from the beginning, but the interaction is performed in a shallow way. In this work,
we let utterance-response interaction go deep
by proposing an interaction-over-interaction
network (IoI). The model performs matching by stacking multiple interaction blocks
in which residual information from one time
of interaction initiates the interaction process
again. Thus, matching information within an
utterance-response pair is extracted from the
interaction of the pair in an iterative fashion,
and the information flows along the chain of
the blocks via representations. Evaluation results on three benchmark data sets indicate that
IoI can significantly outperform state-of-theart methods in terms of various matching metrics. Through further analysis, we also unveil
how the depth of interaction affects the performance of IoI