Authors: Mustafa Omer Gul, Yoav Artzi
Abstract: Systems with both language comprehension and generation capabilities can
benefit from the tight connection between the two. This work studies coupling
comprehension and generation with focus on continually learning from
interaction with users. We propose techniques to tightly integrate the two
capabilities for both learning and inference. We situate our studies in
two-player reference games, and deploy various models for thousands of
interactions with human users, while learning from interaction feedback
signals. We show dramatic improvements in performance over time, with
comprehension-generation coupling leading to performance improvements up to 26%
in absolute terms and up to 17% higher accuracies compared to a non-coupled
system. Our analysis also shows coupling has substantial qualitative impact on
the system’s language, making it significantly more human-like.
Source: http://arxiv.org/abs/2408.15992v1
