  Multimodal models have been proven to outperform text-based models on
learning semantic word representations. Almost all previous multimodal models
typically treat the representations from different modalities equally. However,
it is obvious that information from different modalities contributes
differently to the meaning of words. This motivates us to build a multimodal
model that can dynamically fuse the semantic representations from different
modalities according to different types of words. To that end, we propose three
novel dynamic fusion methods to assign importance weights to each modality, in
which weights are learned under the weak supervision of word association pairs.
The extensive experiments have demonstrated that the proposed methods
outperform strong unimodal baselines and state-of-the-art multimodal models.
