Multi-grained visual pivot-guided multi-modal neural machine translation with text-aware cross-modal contrastive disentangling.

Abstract

The goal of multi-modal neural machine translation (MNMT) is to incorporate language-agnostic visual information into text to enhance the performance of machine translation. However, due to the inherent differences between image and text, these two modalities inevitably suffer from semantic mismatch problems. To tackle this issue, this paper adopts a multi-grained visual pivot-guided multi-modal fusion strategy with cross-modal contrastive disentangling to eliminate the linguistic gaps between different languages. By using the disentangled multi-grained visual information as a cross-lingual pivot, we can enhance the alignment between different languages and improve the performance of MNMT. We first introduce text-guided stacked cross-modal disentangling modules to progressively disentangle image into two types of visual information: MT-related visual and background information. Then we effectively integrate these two kinds of multi-grained visual elements to assist target sentence generation. Extensive experiments on four benchmark MNMT datasets are conducted, and the results demonstrate that our proposed approach achieves significant improvement over the other state-of-the-art (SOTA) approaches on all test sets. The in-depth analysis highlights the benefits of text-guided cross-modal disentangling and visual pivot-based multi-modal fusion strategies in MNMT. We release the code at https://github.com/nlp-mnmt/ConVisPiv-MNMT.