Supervised Visual Attention for Multimodal Neural Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing studies show that a conventional visual attention mechanism trained in an unsupervised manner is not effective for multimodal neural machine translation. |
| Approach: | They propose a supervised visual attention mechanism for multimodal neural machine translation that captures the relationship between a word and an image region more precisely than a conventional visual attention system. |
| Outcome: | The proposed model improves on English-German and German-English translation tasks and English-Japanese and Japanese-English tasks using the Flickr30k Entities JP dataset. |
Similar Papers
Multimodal Neural Machine Translation: A Survey of the State of the Art (2025.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal neural machine translation (MNMT) is a task that aims to translate text into the target language using neural networks. |
| Approach: | They propose to integrate other modalities with textual data to enhance translation performance. |
| Outcome: | The proposed task aims to integrate visual modality with textual data to improve translation quality. |
A Visual Attention Grounding Neural Model for Multimodal Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to multimodal machine translation do not integrate visual information into the translation process. |
| Approach: | They propose a multimodal machine translation model that utilizes parallel visual and textual information. |
| Outcome: | The proposed model outperforms existing methods on the Multi30K and Ambiguous COCO datasets. |
Multimodal Neural Machine Translation Using Synthetic Images Transformed by Latent Diffusion Model (2023.acl-srw)
Copied to clipboard
| Challenge: | Existing methods to translate source language sentences using images are not optimal for machine translation. |
| Approach: | They propose a new multimodal neural machine translation model using synthetic images transformed by a latent diffusion model. |
| Outcome: | The proposed model improves translation performance on English-German translation tasks using the Multi30k dataset. |
Enhancing Machine Translation with Dependency-Aware Self-Attention (2020.acl-main)
Copied to clipboard
| Challenge: | Currently, most neural machine translation models rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. |
| Approach: | They propose a parameter-free, dependency-aware self-attention mechanism that integrates syntactic knowledge into a Transformer model and propose 'a parameter free approach' they also propose - a novel mechanism that improves translation quality for long sentences and in low-resource scenarios. |
| Outcome: | The proposed approach improves translation quality on English-German and English-Turkish translation tasks and in low-resource scenarios. |
On Vision Features in Multimodal Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Recent work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is given to the quality of vision models. |
| Approach: | They develop a selective attention model to study the patch-level contribution of an image in multimodal machine translation. |
| Outcome: | The proposed model is able to learn translation from the visual modality on probing tasks and is compared with existing models. |
Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies on multi-modal neural machine translation focus on visual information, but text and image may not match exactly, and visual noise is often ignored. |
| Approach: | They propose a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT. |
| Outcome: | The proposed model achieves state-of-the-art scores in all En-De, En-Fr and En-Cs translation tasks. |
An Empirical Investigation of Word Alignment Supervision for Zero-Shot Multilingual Neural Machine Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has highlighted several flaws of MNMT models in zero-shot scenarios where language labels are ignored and the wrong language is generated. |
| Approach: | They propose to combine explicit alignment to language labels with word alignment supervision to improve zero-shot translations. |
| Outcome: | The proposed model improves on three multilingual MT benchmarks. |
Multimodal Transformer for Multimodal Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods to incorporate information from other modality, usually static images, are not considered relative to multimodal machine translation. |
| Approach: | They propose a multimodal self-attention method which learns the representation of images based on the text, which avoids encoding irrelevant information in images. |
| Outcome: | The proposed model outperforms previous studies and competitive baselines in terms of various metrics. |
Synchronous Syntactic Attention for Transformer Neural Machine Translation (2021.acl-srw)
Copied to clipboard
| Challenge: | Existing syntaxbased NMT models use monolingual syntactic information on either side or both. |
| Approach: | They propose a mechanism that synchronizes source-side and target-side syntactic self-attentions by minimizing the difference between target- and target side self- attentions mapped by the encoder-decoder attention matrix. |
| Outcome: | The proposed method improves translation performance on WMT14 En-De, WMT16 En-Ro, and ASPEC Ja-En (up to +0.38 points in BLEU). |
Video-guided Machine Translation with Spatial Hierarchical Attention Network (2021.acl-srw)
Copied to clipboard
| Challenge: | Existing studies use pretrained motion detection models as verb sense ambiguity representations to solve the verb sense problem. |
| Approach: | They propose to use video contents as auxiliary information to address the word sense ambiguity problem in machine translation. |
| Outcome: | Experiments on the VATEX dataset show that the proposed system achieves 35.86 BLEU-4 score, which is 0.51 score higher than the single model of the SOTA method. |