A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
This work addresses a specific bottleneck in multi-modal NMT for translation tasks paired with images, representing an incremental improvement over existing methods.
The paper tackles the problem of insufficient exploitation of fine-grained semantic correspondences between words and visual objects in multi-modal neural machine translation (NMT), proposing a graph-based fusion encoder that achieves superior performance on the Multi30K datasets.
Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images. However, dominant multi-modal NMT models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have potential to refine multi-modal representation learning. To deal with this issue, in this paper, we propose a novel graph-based multi-modal fusion encoder for NMT. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between multi-modal semantic units (words and visual objects). We then stack multiple graph-based multi-modal fusion layers that iteratively perform semantic interactions to learn node representations. Finally, these representations provide an attention-based context vector for the decoder. We evaluate our proposed encoder on the Multi30K datasets. Experimental results and in-depth analysis show the superiority of our multi-modal NMT model.