Abstract
Integrating visual features has been proved useful in language representation learning. Nevertheless, in most existing multi-modality models, alignment of visual and textual data is prerequisite. In this paper, we propose a novel semi-supervised visual integration framework for sentence level language representation. The uniqueness include: 1) the integration is conducted via a semi-supervised approach, which can bring image to textual NLU tasks by pre-training a visualization network, 2) visual representations are dynamically integrated in both training and predicting stages. To verify the efficacy of the proposed framework, we conduct the experiments on the SemEval 2018 Task 11 and reach new state-of-the-art on this reading comprehension task. Considering that the visual integration framework only requires image database, and no extra alignment is required for training and prediction, it provides an efficient and feasible method for multi-modality language learning.
Abstract (translated)
URL
https://arxiv.org/abs/1912.00336
